From 024ca6aa6226d5c30ad3b326ee1bfa72e4409ac2 Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:29 +0000 Subject: [PATCH 01/10] [upstream-sync] .github/workflows/sync-autoresearch.yml from promptslab/awesome-prompt-engineering@3b022d69 [unknown] --- .../.github/workflows/sync-autoresearch.yml | 42 +++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/sync-autoresearch.yml diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/sync-autoresearch.yml b/upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/sync-autoresearch.yml new file mode 100644 index 0000000..a739a06 --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/sync-autoresearch.yml @@ -0,0 +1,42 @@ +--- +title: "Sync Autoresearch" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/.github/workflows/sync-autoresearch.yml +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +name: Sync Autoresearch Section + +on: + schedule: + - cron: '0 6 * * *' # Daily at 06:00 UTC + workflow_dispatch: + +permissions: + contents: write + +jobs: + sync: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + + - name: Run sync script + run: python scripts/sync-autoresearch.py + + - name: Commit and push if changed + run: | + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + git add README.md + git diff --staged --quiet || (git commit -m "chore: sync autoresearch section from upstream" && git push) -- 2.54.0 From 832d110cf70426e305c505254517d68e0d277386 Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:34 +0000 Subject: [PATCH 02/10] [upstream-sync] .github/workflows/update-website.yml from promptslab/awesome-prompt-engineering@3b022d69 [unknown] --- .../.github/workflows/update-website.yml | 57 +++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/update-website.yml diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/update-website.yml b/upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/update-website.yml new file mode 100644 index 0000000..df39cf7 --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/.github/workflows/update-website.yml @@ -0,0 +1,57 @@ +--- +title: "Update Website" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/.github/workflows/update-website.yml +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +name: Update Website + +on: + push: + branches: + - main + paths: + - 'README.md' + workflow_dispatch: + +jobs: + trigger-website-update: + runs-on: ubuntu-latest + steps: + - name: Checkout Awesome-Prompt-Engineering + uses: actions/checkout@v4 + with: + repository: promptslab/Awesome-Prompt-Engineering + + - name: Trigger GitHub Pages Rebuild + run: | + echo "βœ… README.md updated! Website will automatically fetch latest content." + echo "The promptslab.github.io website fetches content directly from this repo." + echo "Users will see updates within 30 minutes (cache duration) or immediately on hard refresh." + + - name: Repository Dispatch to Website Repo (Optional) + if: github.event_name == 'push' + run: | + curl -X POST \ + -H "Accept: application/vnd.github.v3+json" \ + -H "Authorization: token ${{ secrets.WEBSITE_UPDATE_TOKEN }}" \ + https://api.github.com/repos/promptslab/promptslab.github.io/dispatches \ + -d '{"event_type":"content-update"}' + continue-on-error: true + + - name: Summary + run: | + echo "## πŸŽ‰ Website Update Triggered" >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + echo "The website at https://promptslab.github.io will automatically display the latest content." >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + echo "- βœ… README.md changes detected" >> $GITHUB_STEP_SUMMARY + echo "- βœ… Content is fetched directly from GitHub" >> $GITHUB_STEP_SUMMARY + echo "- ⏰ Updates visible within 30 minutes or on hard refresh" >> $GITHUB_STEP_SUMMARY -- 2.54.0 From 2e64aa8c2edef3f7e0b7731496c9e599387ec01b Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:38 +0000 Subject: [PATCH 03/10] [upstream-sync] README.md from promptslab/awesome-prompt-engineering@3b022d69 [catalogue] --- .../catalogue/README.md | 991 ++++++++++++++++++ 1 file changed, 991 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/README.md diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/README.md b/upstream/promptslab-awesome-prompt-engineering/catalogue/README.md new file mode 100644 index 0000000..98bd3de --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/README.md @@ -0,0 +1,991 @@ +--- +title: "Readme" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/README.md +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +

Awesome Prompt Engineering πŸ§™β€β™‚οΈ

+ +

+ +

+ +

+ A hand-curated collection of resources for Prompt Engineering and Context Engineering β€” covering papers, tools, models, APIs, benchmarks, courses, and communities for working with Large Language Models. +

+ +

+https://promptslab.github.io +

+

+ + ``` + Master Prompt Engineering. Join the Course at https://promptslab.github.io + ``` + Awesome + License + PRs Welcome + Community + Last Updated +

+ +--- + +## πŸš€ Start Here + +New to prompt engineering? Follow this path: + +

+ +

+ +1. **Learn the basics** β†’ [ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) (free, ~90 min) +2. **Read the guide** β†’ [Prompt Engineering Guide by DAIR.AI](https://www.promptingguide.ai/) (open-source, comprehensive) +3. **Study provider docs** β†’ [OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering) Β· [Anthropic Prompt Engineering Guide](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) +4. **Understand where the field is heading** β†’ [Anthropic: Effective Context Engineering for AI Agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) +5. **Read the research** β†’ [The Prompt Report](https://arxiv.org/abs/2406.06608) β€” taxonomy of 58+ prompting techniques from 1,500+ papers + +--- + +## Table of Contents + +- [Papers](#papers) + - [Major Surveys](#major-surveys) + - [Prompt Optimization and Automatic Prompting](#prompt-optimization-and-automatic-prompting) + - [Prompt Compression](#prompt-compression) + - [Reasoning Advances](#reasoning-advances) + - [In-Context Learning](#in-context-learning) + - [Agentic Prompting and Multi-Agent Systems](#agentic-prompting-and-multi-agent-systems) + - [Multimodal Prompting](#multimodal-prompting) + - [Structured Output and Format Control](#structured-output-and-format-control) + - [Prompt Injection and Security](#prompt-injection-and-security) + - [Applications of Prompt Engineering](#applications-of-prompt-engineering) + - [Text-to-Image Generation](#text-to-image-generation) + - [Text-to-Music/Audio Generation](#text-to-musicaudio-generation) + - [Foundational Papers (Pre-2024)](#foundational-papers-pre-2024) +- [Tools and Code](#tools-and-code) + - [Prompt Management and Testing](#prompt-management-and-testing) + - [LLM Evaluation Tools](#llm-evaluation-tools) + - [Agent Frameworks](#agent-frameworks) + - [Prompt Optimization Tools](#prompt-optimization-tools) + - [Red Teaming and Prompt Security](#red-teaming-and-prompt-security) + - [MCP (Model Context Protocol)](#mcp-model-context-protocol) + - [Vibe Coding and AI Coding Assistants](#vibe-coding-and-ai-coding-assistants) + - [CLI-Based Coding Agents](#cli-based-coding-agents) + - [AI Code Editors / IDEs](#ai-code-editors--ides) + - [IDE Extensions / Plugins](#ide-extensions--plugins) + - [AI Coding Platforms / Cloud Agents](#ai-coding-platforms--cloud-agents) + - [Open-Source Coding Agent Frameworks](#open-source-coding-agent-frameworks) + - [Other Notable Repositories](#other-notable-repositories) +- [APIs](#apis) +- [Datasets and Benchmarks](#datasets-and-benchmarks) +- [Models](#models) +- [AI Content Detectors](#ai-content-detectors) +- [Books](#books) +- [Courses](#courses) +- [Tutorials and Guides](#tutorials-and-guides) +- [Videos](#videos) +- [Communities](#communities) +- [Autonomous Research & Self-Improving Agents](#autonomous-research--self-improving-agents) +- [How to Contribute](#how-to-contribute) + +--- + +## Papers +πŸ“„ + +### Major Surveys + +- [The Prompt Report: A Systematic Survey of Prompting Techniques](https://arxiv.org/abs/2406.06608) [2024] β€” Most comprehensive survey: taxonomy of 58 text and 40 multimodal prompting techniques from 1,500+ papers. Co-authored with OpenAI, Microsoft, Google, Stanford. +- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://arxiv.org/abs/2402.07927) [2024] β€” 44 techniques across application areas with per-task performance summaries. +- [A Survey of Prompt Engineering Methods in LLMs for Different NLP Tasks](https://arxiv.org/abs/2407.12994) [2024] β€” 39 prompting methods across 29 NLP tasks. +- [A Survey of Automatic Prompt Engineering: An Optimization Perspective](https://arxiv.org/abs/2502.11560) [2025] β€” Formalizes auto-PE methods as discrete/continuous/hybrid optimization problems. +- [Efficient Prompting Methods for Large Language Models: A Survey](https://arxiv.org/abs/2404.01077) [2024] β€” Survey of efficiency-oriented prompting (compression, optimization, APE) for reducing compute and latency. +- [Navigate through Enigmatic Labyrinth: A Survey of Chain of Thought Reasoning](https://arxiv.org/abs/2309.15402) [2023, ACL 2024] β€” Systematic CoT survey. +- [Demystifying Chains, Trees, and Graphs of Thoughts](https://arxiv.org/abs/2401.14295) [2024] β€” Unified framework for multi-prompt reasoning topologies. +- [Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey](https://arxiv.org/abs/2401.14043) [2024] β€” Focuses on prompts designed around explicit task goals. +- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning LLMs](https://arxiv.org/abs/2503.09567) [2025] β€” Distinguishes Long CoT from Short CoT in o1/R1-era models. + +### Prompt Optimization and Automatic Prompting + +- [OPRO: Large Language Models as Optimizers](https://arxiv.org/abs/2309.03409) [2023, NeurIPS 2024] β€” Uses LLMs as optimizers via meta-prompts; optimized prompts outperform human-designed ones by up to 50% on BBH. +- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714) [2023, ICLR 2024] β€” Framework for programming (not prompting) LLMs with automatic prompt optimization. +- [MIPRO: Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://arxiv.org/abs/2406.11695) [2024, EMNLP 2024] β€” Bayesian optimization for multi-stage LM programs; up to 13% accuracy gains. +- [TextGrad: Automatic "Differentiation" via Text](https://arxiv.org/abs/2406.07496) [2024] β€” Treats compound AI systems as computation graphs with textual feedback as gradients. Published in Nature. +- [EvoPrompt](https://arxiv.org/abs/2309.08532) [2023, ACL 2024] β€” Evolutionary algorithm approach for automatically optimizing discrete prompts. +- [Meta Prompting for AI Systems](https://arxiv.org/abs/2311.11482) [2023, ICLR 2024 Workshop] β€” Example-agnostic structural templates formalized using category theory. +- [Prompt Engineering a Prompt Engineer (PEΒ²)](https://arxiv.org/abs/2311.05661) [2024, ACL Findings] β€” Uses LLMs to meta-prompt themselves, refining prompts with step-by-step templates to significantly improve reasoning. +- [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) [2022] β€” Automatic prompt generation via APE. +- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning](https://arxiv.org/abs/2302.03668) [2023] +- [SPO: Self-Supervised Prompt Optimization](https://arxiv.org/abs/2502.06855) [2025] β€” Competitive performance at 1–6% of the cost of prior methods. + +### Prompt Compression + +- [LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://arxiv.org/abs/2403.12968) [2024, ACL 2024] β€” 3x–6x faster than LLMLingua with GPT-4 data distillation. +- [LongLLMLingua](https://arxiv.org/abs/2310.06839) [2023, ACL 2024] β€” Question-aware compression for long contexts; 21.4% performance boost with 4x fewer tokens. +- [Prompt Compression for Large Language Models: A Survey](https://arxiv.org/abs/2410.12388) [2024] β€” Comprehensive survey of hard and soft prompt compression methods. + +### Reasoning Advances + +- [Scaling LLM Test-Time Compute Optimally](https://arxiv.org/abs/2408.03314) [2024] β€” Shows optimal test-time compute allocation can outperform 14x larger models. +- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://arxiv.org/abs/2501.12948) [2025] β€” Pure RL-trained reasoning model matching o1; open-source with distilled variants. +- [s1: Simple Test-Time Scaling](https://arxiv.org/abs/2501.19393) [2025] β€” SFT on just 1,000 examples creates competitive reasoning model via "budget forcing." +- [Reasoning Language Models: A Blueprint](https://arxiv.org/abs/2501.11223) [2025] β€” Systematic framework organizing reasoning LM approaches. +- [Demystifying Long Chain-of-Thought Reasoning in LLMs](https://arxiv.org/abs/2502.03373) [2025] β€” Analyzes long CoT behavior in modern reasoning models. +- [Graph of Thoughts: Solving Elaborate Problems with LLMs](https://arxiv.org/abs/2308.09687) [2023, AAAI 2024] β€” Models thoughts as arbitrary graphs; 62% quality improvement over ToT on sorting. +- [Tree of Thoughts: Deliberate Problem Solving with LLMs](https://arxiv.org/abs/2305.10601) [2023, NeurIPS 2023] β€” Tree search over reasoning paths. +- [Everything of Thoughts](https://arxiv.org/abs/2311.04254) [2023] β€” Integrates CoT, ToT, and external solvers via MCTS. +- [Skeleton-of-Thought](https://arxiv.org/abs/2307.15337) [2023] β€” Parallel decoding via answer skeleton generation for up to 2.69x speedup. +- [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) [2022] β€” The foundational CoT paper. +- [Self-Consistency Improves Chain of Thought Reasoning](https://arxiv.org/abs/2203.11171) [2022] β€” Aggregating multiple CoT outputs for reliability. +- [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) [2022] β€” "Let's think step by step" as a zero-shot reasoning trigger. +- [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) [2022] β€” Interleaving reasoning and tool use. + +### In-Context Learning + +- [Many-Shot In-Context Learning](https://arxiv.org/abs/2404.11018) [2024, NeurIPS 2024 Spotlight] β€” Significant gains scaling ICL to hundreds/thousands of examples; introduces Reinforced and Unsupervised ICL. +- [Many-Shot In-Context Learning in Multimodal Foundation Models](https://arxiv.org/abs/2405.09798) [2024] β€” Scales multimodal ICL to ~2,000 examples across 14 datasets. +- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/abs/2202.12837) [2022] +- [Fantastically Ordered Prompts and Where to Find Them](https://arxiv.org/abs/2104.08786) [2021] β€” Overcoming few-shot prompt order sensitivity. +- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) [2021] + +### Agentic Prompting and Multi-Agent Systems + +- [Agentic Large Language Models: A Survey](https://arxiv.org/abs/2503.23037) [2025] β€” Comprehensive survey organizing agentic LLMs by reasoning, acting, and interacting capabilities. +- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://arxiv.org/abs/2402.01680) [2024] β€” Covers profiling, communication, and growth mechanisms. +- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://arxiv.org/abs/2501.06322) [2025] β€” Reviews debate and cooperation strategies in LLM-based multi-agent systems. +- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155) [2023] β€” Microsoft's foundational multi-agent framework paper. +- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-World APIs](https://arxiv.org/abs/2307.16789) [2023, ICLR 2024] β€” Trains LLMs to use massive real-world API collections. +- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) [2023, ICLR 2024] β€” The benchmark driving agentic coding progress. +- [AgentBench: Evaluating LLMs as Agents](https://arxiv.org/abs/2308.03688) [2023, ICLR 2024] β€” Benchmark across 8 environments. +- [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) [2023] β€” Offloading computation to code interpreters. + +### Multimodal Prompting + +- [Visual Prompting in Multimodal Large Language Models: A Survey](https://arxiv.org/abs/2409.15310) [2024] β€” First comprehensive survey on visual prompting methods in MLLMs. +- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V](https://arxiv.org/abs/2310.11441) [2023] β€” Visual markers dramatically improve visual grounding. +- [A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks](https://arxiv.org/abs/2411.06284) [2024] β€” Covers text, image, video, audio MLLMs. +- [Multimodal Chain-of-Thought Reasoning in Language Models](https://arxiv.org/abs/2302.00923) [2023] +- [From Prompt Engineering to Prompt Craft](https://arxiv.org/abs/2411.13422) [2024] β€” Design-research view of prompt "craft" for diffusion models. + +### Structured Output and Format Control + +- [Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of LLMs](https://arxiv.org/abs/2408.02442) [2024] β€” Examines how constraining outputs to structured formats impacts reasoning performance. +- [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) [2023] +- [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) [2022] + +### Prompt Injection and Security + +- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://arxiv.org/abs/2310.12815) [2023, USENIX Security 2024] β€” Formal framework with systematic evaluation of 5 attacks and 10 defenses across 10 LLMs. +- [The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions](https://arxiv.org/abs/2404.13208) [2024] β€” OpenAI's priority-level training for injection defense. +- [AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses](https://arxiv.org/abs/2406.13352) [2024] β€” Realistic agent scenario benchmark. +- [InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated LLM Agents](https://arxiv.org/abs/2403.02691) [2024] +- [SecAlign: Defending Against Prompt Injection with Preference Optimization](https://arxiv.org/abs/2410.05451) [2024] β€” DPO-based defense. +- [WASP: Benchmarking Web Agent Security Against Prompt Injection](https://arxiv.org/abs/2504.18575) [2025] β€” Security benchmark for web/computer-use agents. +- [Many-Shot Jailbreaking](https://www.anthropic.com/research/many-shot-jailbreaking) [2024] β€” Scaling harmful examples in long-context windows enables jailbreaking (Anthropic Technical Report). +- [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) [2022] +- [Ignore Previous Prompt: Attack Techniques For Language Models](https://arxiv.org/abs/2211.09527) [2022] +- [Artificial Intelligence and Cybersecurity: Documented Risks, Enterprise Guardrails, and Emerging Threats in 2024–2025](https://www.ijfmr.com/research-paper.php?id=62200) [2025] β€” Survey of real prompt-injection incidents with practical governance prompt patterns. + +### Applications of Prompt Engineering + +- [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://arxiv.org/abs/2311.04205) [2023] +- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) [2023] +- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems](https://arxiv.org/abs/2210.15157) [2022] +- [Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation](https://arxiv.org/abs/2302.01441) [2023] +- [PLACES: Prompting Language Models for Social Conversation Synthesis](https://arxiv.org/abs/2302.03269) [2023] +- [Medical Image Segmentation Using Transformer Encoders and Prompt-Based Learning: A Systematic Review](https://ieeexplore.ieee.org/document/11313186/) [2025] +- [TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning](https://arxiv.org/abs/2506.10380) [2025] β€” SQL-based interface preserving tabular structure for multi-hop queries. + +### Text-to-Image Generation + +- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) [2022] +- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://arxiv.org/abs/2109.06977) [2021] +- [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) [2021] +- [DALLΒ·E: Creating Images from Text](https://arxiv.org/abs/2102.12092) [2021] +- [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) [2022] + +### Text-to-Music/Audio Generation + +- [MusicLM: Generating Music From Text](https://arxiv.org/abs/2301.11325) [2023] +- [ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models](https://arxiv.org/pdf/2302.04456) [2023] +- [AudioLM: A Language Modeling Approach to Audio Generation](https://arxiv.org/pdf/2209.03143) [2023] +- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://arxiv.org/pdf/2301.12661.pdf) [2023] + +### Foundational Papers (Pre-2024) + +These papers established the core concepts that modern prompt engineering builds on: + +- [Language Models are Few-Shot Learners (GPT-3)](https://arxiv.org/abs/2005.14165) [2020] β€” Demonstrated few-shot prompting at scale. +- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) [2021] +- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) [2021] +- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) [2021] +- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) [2021] +- [Generated Knowledge Prompting for Commonsense Reasoning](https://arxiv.org/abs/2110.08387) [2021] +- [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295) [2021] +- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) [2020] +- [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/) [2020] +- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) [2023] +- [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for LLMs](https://arxiv.org/abs/2302.00618) [2023] +- [Progressive Prompts: Continual Learning for Language Models](https://arxiv.org/abs/2301.12314) [2023] +- [Successive Prompting for Decompleting Complex Questions](https://arxiv.org/abs/2212.04092) [2022] +- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) [2022] +- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) [2022] +- [Ask Me Anything: A Simple Strategy for Prompting Language Models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) [2022] +- [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) [2022] +- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://arxiv.org/abs/2212.08061) [2022] + +--- + +## Tools and Code +πŸ”§ + +### Prompt Management and Testing + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **Promptfoo** | Open-source CLI for testing, evaluating, and red-teaming LLM prompts. YAML configs, CI/CD integration, adversarial testing. ~9K+ ⭐ | [GitHub](https://github.com/promptfoo/promptfoo) | +| **Promptify** | Solve NLP Problems with LLM's & Easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify | [[Github]](https://github.com/promptslab/Promptify) | +| **Agenta** | Open-source LLM developer platform for prompt management, evaluation, human feedback, and deployment. | [GitHub](https://github.com/Agenta-AI/agenta) | +| **PromptLayer** | Version, test, and monitor every prompt and agent with robust evals, tracing, and regression sets. | [Website](https://promptlayer.com/) | +| **Helicone** | Production prompt monitoring and optimization platform. | [Website](https://helicone.ai/) | +| **LangGPT** | Framework for structured and meta-prompt design. 10K+ ⭐ | [GitHub](https://github.com/langgpt/LangGPT) | +| **ChainForge** | Visual toolkit for building, testing, and comparing LLM prompt responses without code. | [GitHub](https://github.com/ianarawjo/ChainForge) | +| **LMQL** | A query language for LLMs making complex prompt logic programmable. | [GitHub](https://github.com/eth-sri/lmql) | +| **Promptotype** | Platform for developing, testing, and managing structured LLM prompts. | [Website](https://www.promptotype.io) | +| **PromptPanda** | AI-powered prompt management system for streamlining prompt workflows. | [Website](https://promptpanda.io) | +| **Promptimize AI** | Browser extension to automatically improve user prompts for any AI model. | [Website](https://promptimize.ai) | +| **PROMPTMETHEUS** | Web-based "Prompt Engineering IDE" for iteratively creating and running prompts. | [Website](https://promptmetheus.com) | +| **Better Prompt** | Test suite for LLM prompts before pushing to production. | [GitHub](https://github.com/krrishdholakia/betterprompt) | +| **OpenPrompt** | Open-source framework for prompt-learning research. | [GitHub](https://github.com/thunlp/OpenPrompt) | +| **Prompt Source** | Toolkit for creating, sharing, and using natural language prompts. | [GitHub](https://github.com/bigscience-workshop/promptsource) | +| **Prompt Engine** | NPM utility library for creating and maintaining prompts for LLMs (Microsoft). | [GitHub](https://github.com/microsoft/prompt-engine) | +| **PromptInject** | Framework for quantitative analysis of LLM robustness to adversarial prompt attacks. | [GitHub](https://github.com/agencyenterprise/PromptInject) | +| **LynxPrompt** | Self-hostable platform for managing AI IDE config files (.cursorrules, CLAUDE.md, copilot-instructions.md). Web UI, REST API, CLI, and federated blueprint marketplace for 30+ AI coding assistants. | [GitHub](https://github.com/GeiserX/LynxPrompt) | +| **flompt** | Visual AI prompt builder that decomposes prompts into 12 semantic blocks (role, context, constraints, examples, etc.) and compiles them into optimized XML. Browser extension for ChatGPT/Claude/Gemini, and MCP server for Claude Code agents. Free, open-source. | [Website](https://flompt.dev) | + +### LLM Evaluation Tools + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **DeepEval** | Open-source evaluation framework covering RAG, agents, and conversations with CI/CD integration. ~7K+ ⭐ | [GitHub](https://github.com/confident-ai/deepeval) | +| **Ragas** | RAG evaluation with knowledge-graph-based test set generation and 30+ metrics. ~8K+ ⭐ | [GitHub](https://github.com/explodinggradients/ragas) | +| **LangSmith** | LangChain's platform for debugging, testing, evaluating, and monitoring LLM applications. | [Website](https://smith.langchain.com/) | +| **Langfuse** | Open-source LLM observability with tracing, prompt management, and human annotation. ~7K+ ⭐ | [GitHub](https://github.com/langfuse/langfuse) | +| **Braintrust** | End-to-end AI evaluation platform, SOC2 Type II certified. | [Website](https://www.braintrust.dev/) | +| **Arize AI / Phoenix** | Real-time LLM monitoring with drift detection and tracing. | [GitHub](https://github.com/Arize-ai/phoenix) | +| **TruLens** | Evaluating and explaining LLM apps; tracks hallucinations, relevance, groundedness. | [GitHub](https://github.com/truera/trulens) | +| **InspectAI** | Purpose-built for evaluating agents against benchmarks (UK AISI). | [GitHub](https://github.com/UKGovernmentBEIS/inspect_ai) | +| **Opik** | Evaluate, test, and ship LLM applications across dev and production lifecycles. | [GitHub](https://github.com/comet-ml/opik) | +| **EvalView** | CLI tool for testing multi-step AI agents with YAML test cases, regression detection, and production monitoring. |[GitHub](https://github.com/hidai25/eval-view) | + +### Agent Frameworks + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **LangChain / LangGraph** | Most widely adopted LLM app framework; LangGraph adds graph-based multi-step agent workflows. ~100K+ / ~10K+ ⭐ | [GitHub](https://github.com/langchain-ai/langchain) Β· [LangGraph](https://github.com/langchain-ai/langgraph) | +| **CrewAI** | Role-playing AI agent orchestration with 700+ integrations. ~44K+ ⭐ | [GitHub](https://github.com/crewAIInc/crewAI) | +| **AutoGen (AG2)** | Microsoft's multi-agent conversational framework. ~40K+ ⭐ | [GitHub](https://github.com/microsoft/autogen) | +| **DSPy** | Stanford's framework for programming LLMs with automatic prompt/weight optimization. ~22K+ ⭐ | [GitHub](https://github.com/stanfordnlp/dspy) | +| **OpenAI Agents SDK** | Official agent framework with function calling, guardrails, and handoffs. ~10K+ ⭐ | [GitHub](https://github.com/openai/openai-agents-python) | +| **Semantic Kernel** | Microsoft's AI framework powering M365 Copilot; C#, Python, Java. ~24K+ ⭐ | [GitHub](https://github.com/microsoft/semantic-kernel) | +| **LlamaIndex** | Data framework for RAG and agent capabilities. ~40K+ ⭐ | [GitHub](https://github.com/run-llama/llama_index) | +| **Haystack** | Open-source NLP framework with pipeline architecture for RAG and agents. ~20K+ ⭐ | [GitHub](https://github.com/deepset-ai/haystack) | +| **Agno (formerly Phidata)** | Python agent framework with microsecond instantiation. ~20K+ ⭐ | [GitHub](https://github.com/agno-agi/agno) | +| **Smolagents** | Hugging Face's minimalist code-centric agent framework (~1000 LOC). ~15K+ ⭐ | [GitHub](https://github.com/huggingface/smolagents) | +| **Pydantic AI** | Type-safe agent framework using Pydantic for structured validation. ~8K+ ⭐ | [GitHub](https://github.com/pydantic/pydantic-ai) | +| **Mastra** | TypeScript AI agent framework with assistants, RAG, and observability. ~20K+ ⭐ | [GitHub](https://github.com/mastra-ai/mastra) | +| **Google ADK** | Agent Development Kit deeply integrated with Gemini and Google Cloud. | [GitHub](https://github.com/google/adk-python) | +| **Strands Agents (AWS)** | Model-agnostic framework with deep AWS integrations. | [GitHub](https://github.com/strands-agents/sdk-python) | +| **Langflow** | Node-based visual agent builder with drag-and-drop. ~50K+ ⭐ | [GitHub](https://github.com/langflow-ai/langflow) | +| **n8n** | Workflow automation with AI agent capabilities and 400+ integrations. ~60K+ ⭐ | [GitHub](https://github.com/n8n-io/n8n) | +| **Dify** | All-in-one backend for agentic workflows with tool-using agents and RAG. | [GitHub](https://github.com/langgenius/dify) | +| **PraisonAI** | Multi-AI Agents framework with 100+ LLM support, MCP integration, and built-in memory. | [GitHub](https://github.com/MervinPraison/PraisonAI) | +| **Neurolink** | Multi-provider AI agent framework unifying 12+ providers with workflow orchestration. | [GitHub](https://github.com/juspay/neurolink) | +| **Composio** | Connect 100+ tools to AI agents with zero setup. | [GitHub](https://github.com/composiohq/composio) | + +### Prompt Optimization Tools + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **DSPy** | Multiple optimizers (MIPROv2, BootstrapFewShot, COPRO) for automatic prompt tuning. ~22K+ ⭐ | [GitHub](https://github.com/stanfordnlp/dspy) | +| **TextGrad** | Automatic differentiation via text (Stanford). ~2K+ ⭐ | [GitHub](https://github.com/zou-group/textgrad) | +| **OPRO** | Google DeepMind's optimization by prompting. | [GitHub](https://github.com/google-deepmind/opro) | + +### Red Teaming and Prompt Security + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **Garak (NVIDIA)** | LLM vulnerability scanner for hallucination, injection, and jailbreaks β€” the "nmap for LLMs." ~3K+ ⭐ | [GitHub](https://github.com/NVIDIA/garak) | +| **PyRIT (Microsoft)** | Python Risk Identification Tool for automated red-teaming. ~3K+ ⭐ | [GitHub](https://github.com/Azure/PyRIT) | +| **DeepTeam** | 40+ vulnerabilities, 10+ attack methods, OWASP Top 10 support. | [GitHub](https://github.com/confident-ai/deepteam) | +| **LLM Guard** | Security toolkit for LLM I/O validation. ~2K+ ⭐ | [GitHub](https://github.com/protectai/llm-guard) | +| **NeMo Guardrails (NVIDIA)** | Programmable guardrails for conversational systems. ~5K+ ⭐ | [GitHub](https://github.com/NVIDIA/NeMo-Guardrails) | +| **Guardrails AI** | Define strict output formats (JSON schemas) to ensure system reliability. | [Website](https://www.guardrailsai.com) | +| **Lakera** | AI security platform for real-time prompt injection detection. | [Website](https://lakera.ai/) | +| **Purple Llama (Meta)** | Open-source LLM safety evaluation including CyberSecEval. | [GitHub](https://github.com/meta-llama/PurpleLlama) | +| **GPTFuzz** | Automated jailbreak template generation achieving >90% success rates. | [GitHub](https://github.com/sherdencooper/GPTFuzz) | +| **Rebuff** | Open-source tool for detection and prevention of prompt injection. | [GitHub](https://github.com/protectai/rebuff) | +| **AgentSeal** | "Open-source scanner that runs 150 attack probes to test AI agents for prompt injection and extraction vulnerabilities." | [GitHub](https://github.com/agentseal/agentseal) | + +### MCP (Model Context Protocol) + +MCP is an open standard developed by Anthropic (Nov 2024, donated to Linux Foundation Dec 2025) for connecting AI assistants to external data sources and tools through a standardized interface. It has **97M+ monthly SDK downloads** and has been adopted by GitHub, Google, and most major AI providers. + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **MCP Specification** | The core protocol specification and SDKs. ~15K+ ⭐ | [GitHub](https://github.com/modelcontextprotocol/modelcontextprotocol) | +| **MCP Reference Servers** | Official implementations: fetch, filesystem, GitHub, Slack, Postgres. | [GitHub](https://github.com/modelcontextprotocol/servers) | +| **FastMCP (Python)** | High-level Pythonic framework for building MCP servers. ~5K+ ⭐ | [GitHub](https://github.com/jlowin/fastmcp) | +| **GitHub MCP Server** | GitHub's official MCP server for repo, issue, PR, and Actions interaction. ~15K+ ⭐ | [GitHub](https://github.com/github/github-mcp-server) | +| **Awesome MCP Servers** | Curated list of 10,000+ community MCP servers. ~30K+ ⭐ | [GitHub](https://github.com/punkpeye/awesome-mcp-servers) | +| **Context7** | MCP server providing version-specific documentation to reduce code hallucination. | [GitHub](https://github.com/upstash/context7) | +| **GitMCP** | Creates remote MCP servers for any GitHub repo by changing the domain. | [Website](https://gitmcp.io/) | +| **MCP Inspector** | Visual testing tool for MCP server development. | [GitHub](https://github.com/modelcontextprotocol/inspector) | + +### Vibe Coding and AI Coding Assistants + +> 🟒 = Open Source Β· πŸ”΅ = Commercial Β· 🟣 = Open Source + Commercial (open core with paid cloud/API) + +#### CLI-Based Coding Agents + +Terminal-native agentic tools that understand your codebase and execute multi-step tasks. + +| Name | Description | Type | Link | +|:-----|:-----------|:----:|:----:| +| **Claude Code** | Anthropic's agentic coding CLI; understands full codebases and executes complex multi-step tasks via natural language. | πŸ”΅ | [Docs](https://docs.anthropic.com/en/docs/claude-code) | +| **OpenAI Codex CLI** | Open-source terminal coding agent from OpenAI; lightweight, local-first, with sandboxed code execution. ~68K+ ⭐ | 🟣 | [GitHub](https://github.com/openai/codex) | +| **Gemini CLI** | Google's open-source terminal AI agent with 1M-token context window and Google Search grounding. ~96K+ ⭐ | 🟣 | [GitHub](https://github.com/google-gemini/gemini-cli) | +| **Qwen Code** | Open-source terminal AI agent optimized for Qwen3-Coder; multi-protocol support (OpenAI/Anthropic/Gemini APIs), 1,000 free requests/day. ~21K+ ⭐ | 🟒 | [GitHub](https://github.com/QwenLM/qwen-code) | +| **Aider** | AI pair programming in terminal with deep Git integration; maps entire codebases and auto-commits changes. ~42K+ ⭐ | 🟒 | [GitHub](https://github.com/Aider-AI/aider) | +| **OpenCode** | Powerful open-source AI coding agent with beautiful TUI; supports nearly all AI model providers. ~120K+ ⭐ | 🟒 | [GitHub](https://github.com/opencode-ai/opencode) | +| **Goose** | Extensible open-source AI agent from Block (Square/Cash App); installs, executes, edits, and tests with any LLM. ~29K+ ⭐ | 🟒 | [GitHub](https://github.com/block/goose) | +| **Crush** | Glamorous agentic coding agent from Charmbracelet with multi-model support, LSP integration, and beautiful terminal UI. ~9K+ ⭐ | 🟒 | [GitHub](https://github.com/charmbracelet/crush) | +| **Amazon Q Developer CLI** | Agentic chat experience in terminal from AWS; transitioning to Kiro CLI. | 🟣 | [GitHub](https://github.com/aws/amazon-q-developer-cli) | +| **Amp** | Sourcegraph's agentic coding tool (Cody successor); works across CLI and IDE. | πŸ”΅ | [Website](https://ampcode.com) | +| **Junie CLI** | JetBrains' LLM-agnostic coding agent CLI (beta 2026); supports all major model providers. | πŸ”΅ | [Website](https://www.jetbrains.com/junie/) | +| **Autohand Code CLI** | Self-evolving autonomous terminal coding agent with multi-provider LLM support, 40+ tools, and modular skills system. | 🟒 | [GitHub](https://github.com/autohandai/code-cli) | + +#### AI Code Editors / IDEs + +Standalone editors or IDE forks with deep AI integration. + +| Name | Description | Type | Link | +|:-----|:-----------|:----:|:----:| +| **Cursor** | Leading AI-native code editor (VS Code fork); Composer generates entire apps from natural language, agentic multi-file edits. | πŸ”΅ | [Website](https://cursor.com) | +| **Windsurf** | AI-powered IDE (VS Code fork) with proprietary Cascade agent and SWE-1.5 model; acquired by Cognition AI. | πŸ”΅ | [Website](https://windsurf.com) | +| **Zed** | High-performance editor in Rust with native AI features, Zeta edit prediction, and Agent Client Protocol support. ~77K+ ⭐ | 🟒 | [GitHub](https://github.com/zed-industries/zed) | +| **Trae** | Free AI-powered IDE from ByteDance ("The Real AI Engineer") with Builder Mode; provides free access to Claude, GPT-4o, and DeepSeek. | πŸ”΅ | [Website](https://www.trae.ai) | +| **Google Antigravity** | Google's agent-first IDE (VS Code fork) with Manager view for orchestrating multiple agents in parallel; powered by Gemini. | πŸ”΅ | [Website](https://antigravity.google) | +| **Kiro** | AWS's spec-driven agentic AI IDE (VS Code fork); turns prompts into specs, then working code, docs, and tests. | πŸ”΅ | [Website](https://kiro.dev) | +| **PearAI** | Open-source AI code editor (VS Code fork) with Continue-based chat and completions. ~40K+ ⭐ | 🟒 | [GitHub](https://github.com/trypear/pearai-app) | +| **Void** | Open-source Cursor alternative (VS Code fork); any model or local hosting with change visualization. ~28K+ ⭐ | 🟒 | [GitHub](https://github.com/voideditor/void) | +| **Melty** | Open-source chat-first AI code editor with multi-file editing and deep Git integration. ~7K+ ⭐ | 🟒 | [GitHub](https://github.com/meltylabs/melty) | +| **Emdash** | Open-source agentic dev environment (YC W26) for running multiple coding agents in parallel in isolated Git worktrees. | 🟒 | [GitHub](https://github.com/generalaction/emdash) | + +#### IDE Extensions / Plugins + +Plugins for VS Code, JetBrains, Neovim, and other editors. + +| Name | Description | Type | Link | +|:-----|:-----------|:----:|:----:| +| **GitHub Copilot** | Most widely adopted AI coding assistant; inline completions, chat, and agentic coding agent across VS Code, JetBrains, Neovim. | πŸ”΅ | [Website](https://github.com/features/copilot) | +| **Cline** | Autonomous coding agent in VS Code with human-in-the-loop approvals; file editing, terminal commands, and browser use. ~59K+ ⭐ | 🟒 | [GitHub](https://github.com/cline/cline) | +| **Continue** | Open-source VS Code and JetBrains extension for creating custom, modular AI dev systems; any model. ~32K+ ⭐ | 🟒 | [GitHub](https://github.com/continuedev/continue) | +| **Cody** | Sourcegraph-powered AI assistant that pulls context from local and remote codebases; VS Code, JetBrains, Visual Studio. | πŸ”΅ | [Website](https://sourcegraph.com/cody) | +| **Codeium** | Free AI coding extension for 40+ IDEs with completions, chat, and search across 70+ languages. | 🟣 | [Website](https://codeium.com) | +| **Amazon Q Developer** | AWS's AI coding assistant with completions, inline chat, and agent mode; deep AWS integration. | 🟣 | [Website](https://aws.amazon.com/q/developer/) | +| **Gemini Code Assist** | Google's IDE extension powered by Gemini with completions, Next Edit Predictions, and inline diffs; free for individuals. | 🟣 | [Website](https://codeassist.google) | +| **Tabnine** | Privacy-focused AI assistant trained on permissive-licensed OSS; supports all major IDEs with on-premises deployment. | πŸ”΅ | [Website](https://www.tabnine.com) | +| **Augment Code** | Enterprise AI coding assistant with 200K-token Context Engine for deep codebase understanding. | πŸ”΅ | [Website](https://www.augmentcode.com) | +| **Qodo** | AI code review and quality platform with multi-agent architecture; test generation, code review, CI/CD enforcement. | 🟣 | [Website](https://www.qodo.ai) | +| **CodeGeeX** | Open-source multilingual code generation model supporting 20+ languages with VS Code and JetBrains extensions. ~11K+ ⭐ | 🟒 | [GitHub](https://github.com/zai-org/CodeGeeX) | +| **Tabby** | Self-hosted open-source AI coding assistant (Copilot alternative); runs entirely on your infrastructure. ~25K+ ⭐ | 🟒 | [GitHub](https://github.com/TabbyML/tabby) | + +#### AI Coding Platforms / Cloud Agents + +Browser-based or cloud-hosted agents that build, test, and deploy autonomously. + +| Name | Description | Type | Link | +|:-----|:-----------|:----:|:----:| +| **Devin** | First fully autonomous cloud-based AI software engineer; plans, codes, tests, and opens PRs independently. | πŸ”΅ | [Website](https://devin.ai) | +| **Replit Agent** | Cloud-native AI agent that autonomously builds, tests, and deploys full-stack apps in-browser; 50+ languages. | πŸ”΅ | [Website](https://replit.com/products/agent) | +| **bolt.new** | AI-powered web dev agent; prompt, run, edit, and deploy full-stack apps directly in the browser via WebContainers. ~15K+ ⭐ | 🟒 | [GitHub](https://github.com/stackblitz/bolt.new) | +| **bolt.diy** | Community fork of bolt.new with extended features and broader LLM flexibility. ~12K+ ⭐ | 🟒 | [GitHub](https://github.com/stackblitz-labs/bolt.diy) | +| **Lovable** | Full-stack apps from natural language with built-in Supabase, auth, and one-click deploy; fastest European startup to $20M ARR. | πŸ”΅ | [Website](https://lovable.dev) | +| **v0** | Vercel's AI platform for generating high-quality React/Next.js UI components from natural language. | πŸ”΅ | [Website](https://v0.dev) | +| **GitHub Copilot Workspace** | Cloud-based coding environment with plan, brainstorm, and repair agents; included with paid Copilot plans. | πŸ”΅ | [Website](https://githubnext.com/projects/copilot-workspace) | +| **Firebase Studio** | Google's agentic cloud-based development environment. | πŸ”΅ | [Website](https://firebase.google.com/studio) | + +#### Open-Source Coding Agent Frameworks + +Frameworks and research projects for building autonomous coding agents. + +| Name | Description | Type | Link | +|:-----|:-----------|:----:|:----:| +| **OpenHands** | Leading open-source platform for cloud coding agents; consistently top on SWE-bench. Formerly OpenDevin. ~69K+ ⭐ | 🟒 | [GitHub](https://github.com/OpenHands/OpenHands) | +| **SWE-agent** | Takes a GitHub issue and automatically fixes it using a custom agent-computer interface. [NeurIPS 2024] ~19K+ ⭐ | 🟒 | [GitHub](https://github.com/SWE-agent/SWE-agent) | +| **Open SWE** | LangChain's async cloud-hosted coding agent framework built on LangGraph with Slack/Linear integration. ~8K+ ⭐ | 🟒 | [GitHub](https://github.com/langchain-ai/open-swe) | +| **Devika** | Open-source agentic software engineer; breaks down instructions, researches, and writes code. Devin alternative. ~18K+ ⭐ | 🟒 | [GitHub](https://github.com/stitionai/devika) | +| **AutoCodeRover** | Autonomous program improvement combining LLMs with fault localization for GitHub issue resolution. ~2.8K+ ⭐ | 🟒 | [GitHub](https://github.com/nus-apr/auto-code-rover) | +| **Agentless** | Simple three-phase approach (localize β†’ repair β†’ validate) to solving software development problems. ~2K+ ⭐ | 🟒 | [GitHub](https://github.com/OpenAutoCoder/Agentless) | +| **Devon** | Open-source pair programmer SWE agent with code writing, planning, and research; supports Claude, GPT-4, Llama, Ollama. ~3.5K+ ⭐ | 🟒 | [GitHub](https://github.com/entropy-research/Devon) | + +### Other Notable Repositories + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **Prompt Engineering Guide (DAIR.AI)** | The definitive open-source guide and resource hub. 3M+ learners. ~55K+ ⭐ | [GitHub](https://github.com/dair-ai/Prompt-Engineering-Guide) | +| **Awesome ChatGPT Prompts / Prompts.chat** | World's largest open-source prompt library. 1000s of prompts for all major models. | [GitHub](https://github.com/f/awesome-chatgpt-prompts) | +| **12-Factor Agents** | Principles for building production-grade LLM-powered software. ~17K+ ⭐ | [GitHub](https://github.com/humanlayer/12-factor-agents) | +| **NirDiamant/Prompt_Engineering** | 22 hands-on Jupyter Notebook tutorials. ~3K+ ⭐ | [GitHub](https://github.com/NirDiamant/Prompt_Engineering) | +| **Context Engineering Repository** | First-principles handbook for moving beyond prompt engineering to context design. | [GitHub](https://github.com/davidkimai/Context-Engineering) | +| **AI Agent System Prompts Library** | Collection of system prompts from production AI coding agents (Claude Code, Gemini CLI, Cline, Aider, Roo Code). | [GitHub](https://github.com/tallesborges/agentic-system-prompts) | +| **Awesome Vibe Coding** | Curated list of 245+ tools and resources for building software through natural language prompts. | [GitHub](https://github.com/taskade/awesome-vibe-coding) | +| **OpenAI Cookbook** | Official recipes for prompts, tools, RAG, and evaluations. | [GitHub](https://github.com/openai/openai-cookbook) | +| **Embedchain** | Framework to create ChatGPT-like bots over your dataset. | [GitHub](https://github.com/embedchain/embedchain) | +| **ThoughtSource** | Framework for the science of machine thinking. | [GitHub](https://github.com/OpenBioLink/ThoughtSource) | +| **Promptext** | Extracts and formats code context for AI prompts with token counting. | [GitHub](https://github.com/1broseidon/promptext) | +| **Price Per Token** | Compare LLM API pricing across 200+ models. | [Website](https://pricepertoken.com/) | +| **OpenPaw** | CLI tool (`npx pawmode`) that turns Claude Code into a personal assistant by generating system prompts (CLAUDE.md + SOUL.md) with personality, memory, and 38 skill routers. | [GitHub](https://github.com/daxaur/openpaw) | +| **Think Better** | Open-source CLI that permanently injects 10 structured decision frameworks (MECE, Issue Trees, Pre-Mortems) and 12 cognitive bias detectors into AI assistant prompts. Go, MIT. | [GitHub](https://github.com/HoangTheQuyen/think-better) | + +--- + +## APIs +πŸ’» + +### OpenAI + +| Model | Context | Price (Input/Output per 1M tokens) | Key Feature | +|:------|:--------|:-----------------------------------|:------------| +| GPT-5.2 / 5.2 Thinking | 400K | $1.75 / $14 | Latest flagship, 90% cached discount, configurable reasoning | +| GPT-5.1 | 400K | $1.25 / $10 | Previous generation flagship | +| GPT-4.1 / 4.1 mini / nano | 1M | $2 / $8 | Best non-reasoning model, 40% faster and 80% cheaper than GPT-4o | +| o3 / o3-pro | 200K | Varies | Reasoning models with native tool use | +| o4-mini | 200K | Cost-efficient | Fast reasoning, best on AIME at its cost class | +| GPT-OSS-120B / 20B | 128K | $0.03 / $0.30 | First open-weight models, Apache 2.0 | + +Key features: Responses API, Agents SDK, Structured Outputs, function calling, prompt caching (90% discount), Batch API (50% discount), MCP support. [Platform Docs](https://platform.openai.com/docs/models) + +### Anthropic (Claude) + +| Model | Context | Price (Input/Output per 1M tokens) | Key Feature | +|:------|:--------|:-----------------------------------|:------------| +| Claude Opus 4.6 | 1M (beta) | $5 / $25 | Most powerful, state-of-the-art coding and agentic tasks | +| Claude Sonnet 4.5 | 200K | $3 / $15 | Best coding model, 61.4% OSWorld (computer use) | +| Claude Haiku 4.5 | 200K | Fast tier | Near-frontier, fastest model class | +| Claude Opus 4 / Sonnet 4 | 200K | $15/$75 (Opus) | Opus: 72.5% SWE-bench, Sonnet 4 powers GitHub Copilot | + +Key features: Extended Thinking with tool use, Computer Use, MCP (originated here), prompt caching, Claude Code CLI, available on AWS Bedrock and Google Vertex AI. [API Docs](https://docs.anthropic.com/) + +### Google (Gemini) + +| Model | Context | Price (Input/Output per 1M tokens) | Key Feature | +|:------|:--------|:-----------------------------------|:------------| +| Gemini 3 Pro Preview | 1M | $2 / $12 | Most intelligent Google model, deployed to 2B+ Search users | +| Gemini 2.5 Pro | 1M | $1.25 / $10 | Best for coding/agentic tasks, thinking model | +| Gemini 2.5 Flash / Flash-Lite | 1M | $0.30/$1.50 Β· $0.10/$0.40 | Price-performance leaders | + +Key features: Thinking (all 2.5+ models), Google Search grounding, code execution, Live API (real-time audio/video), context caching. [Google AI Studio](https://ai.google.dev/) + +### Meta (Llama) + +| Model | Architecture | Context | Key Feature | +|:------|:------------|:--------|:------------| +| Llama 4 Scout | 109B MoE / 17B active | 10M | Fits single H100, multimodal, open-weight | +| Llama 4 Maverick | 400B MoE / 17B active, 128 experts | 1M | Beats GPT-4o, open-weight | +| Llama 3.3 70B | Dense | 128K | Matches Llama 3.1 405B | + +Available on 25+ cloud partners, Hugging Face, and inference APIs. [Llama](https://ai.meta.com/llama/) + +### Other Notable Providers + +| Provider | Description | Link | +|:---------|:-----------|:----:| +| **Mistral AI** | Mistral Large 3 (675B MoE), Devstral 2, Ministral 3. Apache 2.0. | [Website](https://mistral.ai) | +| **DeepSeek** | V3.2 (671B MoE), R1 (reasoning, MIT license). $0.15/$0.75 per 1M tokens. | [Website](https://deepseek.com) | +| **xAI (Grok)** | Grok 4.1 Fast: 2M context, $0.20/$0.50 per 1M tokens. | [Website](https://x.ai) | +| **Cohere** | Command A (111B, 256K context), Embed v4, Rerank 4.0. Excels at RAG. | [Website](https://cohere.com) | +| **Together AI** | 200+ open models with sub-100ms latency. | [Website](https://together.ai) | +| **Groq** | LPU hardware with ~300+ tokens/sec inference. | [Website](https://groq.com) | +| **Fireworks AI** | Fast inference with HIPAA + SOC2 compliance. | [Website](https://fireworks.ai) | +| **OpenRouter** | Unified API for 300+ models from all providers. | [Website](https://openrouter.ai) | +| **Cerebras** | Wafer-scale chips with best total response time. | [Website](https://cerebras.ai) | +| **Perplexity AI** | Search-augmented API with citations. | [Website](https://perplexity.ai) | +| **Amazon Bedrock** | Managed multi-model service with Claude, Llama, Mistral, Cohere. | [Website](https://aws.amazon.com/bedrock/) | +| **Hugging Face Inference** | Access to open models via API. | [Website](https://huggingface.co/docs/api-inference/index) | + +--- + +## Datasets and Benchmarks +πŸ’Ύ + +### Major Benchmarks (2024–2026) + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **Chatbot Arena / LM Arena** | 6M+ user votes for Elo-rated pairwise LLM comparisons. De facto standard for human preference. | [Website](https://lmarena.ai/) | +| **MMLU-Pro** | 12,000+ graduate-level questions across 14 domains. NeurIPS 2024 Spotlight. | [GitHub](https://github.com/TIGER-AI-Lab/MMLU-Pro) | +| **GPQA** | 448 "Google-proof" STEM questions; non-expert validators achieve only 34%. | [arXiv](https://arxiv.org/abs/2311.12022) | +| **SWE-bench Verified** | Human-validated 500-task subset for real-world GitHub issue resolution. | [Website](https://www.swebench.com/) | +| **SWE-bench Pro** | 1,865 tasks across 41 professional repos; best models score only ~23%. | [Leaderboard](https://scale.com/leaderboard/swe_bench_pro_public) | +| **Humanity's Last Exam (HLE)** | 2,500 expert-vetted questions; top AI scores only ~10–30%. | [Website](https://agi.safe.ai/) | +| **BigCodeBench** | 1,140 coding tasks across 7 domains; AI achieves ~35.5% vs. 97% human success. | [Leaderboard](https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard) | +| **LiveBench** | Contamination-resistant with frequently updated questions. | [Paper](https://openreview.net/forum?id=sKYHBTAxVa) | +| **FrontierMath** | Research-level math; AI solves only ~2% of problems. | Research | +| **ARC-AGI v2** | Abstract reasoning measuring fluid intelligence. | Research | +| **IFEval** | Instruction-following evaluation with formatting/content constraints. | [arXiv](https://arxiv.org/abs/2311.07911) | +| **MLE-bench** | OpenAI's ML engineering evaluation via Kaggle-style tasks. | [GitHub](https://github.com/openai/mle-bench) | +| **PaperBench** | Evaluates AI's ability to replicate 20 ICML 2024 papers from scratch. | [GitHub](https://github.com/openai/preparedness) | + +### Leaderboards and Meta-Benchmarks + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **Hugging Face Open LLM Leaderboard v2** | Evaluates open models on MMLU-Pro, GPQA, IFEval, MATH. | [Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) | +| **Artificial Analysis Intelligence Index v3** | Aggregates 10 evaluations. | [Website](https://artificialanalysis.ai/) | +| **SEAL by Scale AI** | Hosts SWE-bench Pro and agentic evaluations. | [Leaderboard](https://scale.com/leaderboard) | + +### Prompt and Instruction Datasets + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **P3 (Public Pool of Prompts)** | Prompt templates for 270+ NLP tasks used to train T0 and similar models. | [HuggingFace](https://huggingface.co/datasets/bigscience/P3) | +| **System Prompts Dataset** | 944 system prompt templates for agent workflows (by Daniel Rosehill, Aug 2025). | [HuggingFace](https://huggingface.co/datasets/danielrosehill/system_prompts) | +| **OpenAssistant Conversations (OASST)** | 161,443 messages in 35 languages with 461,292 quality ratings. | [HuggingFace](https://huggingface.co/datasets/OpenAssistant/oasst1) | +| **UltraChat / UltraFeedback** | Large-scale synthetic instruction and preference datasets for alignment training. | HuggingFace | +| **SoftAge Prompt Engineering Dataset** | 1,000 diverse prompts across 10 categories for benchmarking prompt performance. | HuggingFace | +| **Text Transformation Prompt Library** | Comprehensive collection of text transformation prompts (May 2025). | HuggingFace | +| **Writing Prompts** | ~300K human-written stories paired with prompts from r/WritingPrompts. | [Kaggle](https://www.kaggle.com/datasets/ratthachat/writing-prompts) | +| **Midjourney Prompts** | Text prompts and image URLs scraped from MidJourney's public Discord. | [HuggingFace](https://huggingface.co/datasets/succinctly/midjourney-prompts) | +| **CodeAlpaca-20k** | 20,000 programming instruction-output pairs. | [HuggingFace](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) | +| **ProPEX-RAG** | Dataset for prompt optimization in RAG workflows. | HuggingFace | +| **NanoBanana Trending Prompts** | 1,000+ curated AI image prompts from X/Twitter, ranked by engagement. | [GitHub](https://github.com/jau123/nanobanana-trending-prompts) | + +### Red Teaming and Adversarial Datasets + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **HarmBench** | 510 harmful behaviors across standard, contextual, copyright, and multimodal categories. | [Website](https://safetyprompts.com/) | +| **JailbreakBench** | Open robustness benchmark for jailbreaking with 100 prompts. | Research | +| **AgentHarm** | 110 malicious agent tasks across 11 harm categories. | [arXiv](https://arxiv.org/abs/2410.09024) | +| **DecodingTrust** | 243,877 prompts evaluating trustworthiness across 8 perspectives. | Research | +| **SafetyPrompts.com** | Aggregator tracking 50+ safety/red-teaming datasets. | [Website](https://safetyprompts.com/) | + +--- + +## Models +🧠 + +### Frontier Models (2025–2026) + +| Model | Provider | Context | Key Strength | +|:------|:---------|:--------|:-------------| +| **GPT-5.2** | OpenAI | 400K | General intelligence, 100% AIME 2025 | +| **Claude Opus 4.6** | Anthropic | 1M (beta) | Coding, agentic tasks, extended thinking | +| **Gemini 3 Pro** | Google | 1M | #1 LMArena (~1500 Elo), multimodal | +| **Grok 4.1** | xAI | 2M | #2 LMArena (1483 Elo), low hallucination | +| **Mistral Large 3** | Mistral AI | 256K | Best open-weight (675B MoE/41B active), Apache 2.0 | +| **DeepSeek-V3.2** | DeepSeek | 128K | Best value (671B MoE/37B active), MIT license | +| **Llama 4 Maverick** | Meta | 1M | Beats GPT-4o (400B MoE/17B active), open-weight | + +### Reasoning Models + +| Model | Key Detail | +|:------|:-----------| +| **OpenAI o3 / o3-pro** | 87.7% GPQA Diamond. Native tool use. | +| **OpenAI o4-mini** | Best AIME at its cost class with visual reasoning. | +| **DeepSeek-R1 / R1-0528** | Open-weight, RL-trained. 87.5% on AIME 2025. MIT license. | +| **QwQ (Qwen with Questions)** | 32B reasoning model. Apache 2.0. Comparable to R1. | +| **Gemini 2.5 Pro/Flash (Thinking)** | Built-in reasoning with configurable thinking budget. | +| **Claude Extended Thinking** | Hybrid mode with visible chain-of-thought and tool use. | +| **Phi-4 Reasoning / Plus** | 14B reasoning models rivaling much larger models. Open-weight. | +| **GPT-OSS-120B** | OpenAI's open-weight with CoT. Near-parity with o4-mini. Apache 2.0. | + +### Notable Open-Source Models + +| Model | Provider | Key Detail | +|:------|:---------|:-----------| +| **Qwen3-235B-A22B** | Alibaba | Flagship MoE. Strong reasoning/code/multilingual. Apache 2.0. Most downloaded family on HuggingFace. | +| **Gemma 3** | Google | 270M to 27B. Multimodal. 128K context. 140+ languages. | +| **OLMo 2/3** | Allen AI | Fully open (data, code, weights, logs). OLMo 2 32B surpasses GPT-3.5. Apache 2.0. | +| **SmolLM3-3B** | Hugging Face | Outperforms Llama-3.2-3B. Dual-mode reasoning. 128K context. | +| **Kimi K2** | Moonshot AI | 32B active. Open-weight. Tailored for coding/agentic use. | +| **Llama 4 Scout** | Meta | 109B MoE/17B active. 10M token context. Fits single H100. | + +### Code-Specialized Models + +| Model | Key Detail | +|:------|:-----------| +| **Qwen3-Coder (480B-A35B)** | 69.6% SWE-bench β€” milestone for open-source coding. 256K context. Apache 2.0. | +| **Devstral 2 (123B)** | 72.2% SWE-bench Verified. 7x more cost-efficient than Claude Sonnet. | +| **Codestral 25.01** | Mistral's code model. 80+ languages. Fill-in-the-Middle support. | +| **DeepSeek-Coder-V2** | 236B MoE / 21B active. 338 programming languages. | +| **Qwen 2.5-Coder** | 7B/32B. 92 programming languages. 88.4% HumanEval. Apache 2.0. | + +### Foundational Models (Historical Reference) + +These models established key concepts but are largely superseded for practical use: + +| Model | Provider | Significance | +|:------|:---------|:-------------| +| GLM-130B | Tsinghua | Open bilingual English/Chinese LLM (2023) | +| Falcon 180B | TII | Large open generative model (2023) | +| Mixtral 8x7B | Mistral AI | Pioneered MoE architecture for open models (2023) | +| GPT-NeoX-20B | EleutherAI | Early open autoregressive LLM | +| GPT-J-6B | EleutherAI | Early open causal language model | + +--- + +## AI Content Detectors +πŸ”Ž + +### Leading Commercial Detectors + +| Name | Accuracy | Key Feature | Link | +|:-----|:---------|:------------|:----:| +| **GPTZero** | 99% claimed | 10M+ users, #1 on G2 (2025). Detects GPT-4/5, Gemini, Claude, Llama. Free tier available. | [Website](https://gptzero.me) | +| **Originality.ai** | 98–100% (peer-reviewed) | Consistently rated most accurate. Combines AI detection + plagiarism + fact checking. From $14.95/month. | [Website](https://originality.ai) | +| **Turnitin AI Detection** | 98%+ on unmodified AI text | Dominant in academia. Launched AI bypasser/humanizer detection (Aug 2025). Institutional licensing. | [Website](https://www.turnitin.com/solutions/topics/ai-writing/) | +| **Copyleaks** | 99%+ claimed | Enterprise tool detecting AI in 30+ languages. LMS integrations. | [Website](https://copyleaks.com) | +| **Winston AI** | 99.98% claimed | OCR for scanned documents, AI image/deepfake detection. 11 languages. | [Website](https://gowinston.ai) | +| **Pangram Labs** | 99.3% (COLING 2025) | Highest score in COLING 2025 Shared Task. 100% TPR on "humanized" text. 97.7% adversarial robustness. | [Website](https://www.pangram.com) | + +### Free and Research Detectors + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **Binoculars** | Open-source research detector using cross-perplexity between two LLMs. | [arXiv](https://arxiv.org/abs/2401.12070) | +| **DetectGPT / Fast-DetectGPT** | Statistical method comparing log-probabilities of original text vs. perturbations. | [arXiv](https://arxiv.org/abs/2301.11305) | +| **Openai Detector** | AI classifier for indicating AI-written text (OpenAI Detector Python wrapper) | [[GitHub]](https://github.com/promptslab/openai-detector) | +| **Sapling AI Detector** | Free browser-based detector (up to 2,000 chars). 97% accuracy in some studies. | [Website](https://sapling.ai/) | +| **QuillBot AI Detector** | Free, no sign-up required. | [Website](https://quillbot.com/ai-content-detector) | +| **Writer AI Content Detector** | Free tool with color-coded results. | [Website](https://writer.com/ai-content-detector/) | +| **ZeroGPT** | Popular free detector evaluated in multiple academic studies. | [Website](https://www.zerogpt.com/) | + +### Watermarking Approaches + +| Name | Description | Link | +|:-----|:-----------|:----:| +| **SynthID (Google DeepMind)** | Watermarking for AI text, images, and audio via statistical token sampling. Deployed in Google products. | [Website](https://deepmind.google/technologies/synthid/) | +| **OpenAI Text Watermarking** | Developed but still experimental as of 2025. Research shows fragility concerns. | Experimental | + +**Important caveat:** No detector claims 100% accuracy. Mixed human/AI text remains hardest to detect (50–70% accuracy). Adversarial robustness varies widely. The AI detection market is projected to grow from ~$2.3B (2025) to $15B by 2035. + +--- + +## Books +πŸ“– + +### Prompt Engineering + +| Title | Author(s) | Publisher | Year | +|:------|:----------|:---------|:-----| +| **Prompt Engineering for LLMs** | John Berryman & Albert Ziegler | O'Reilly | 2024 | +| **Prompt Engineering for Generative AI** | James Phoenix & Mike Taylor | O'Reilly | 2024 | +| **Prompt Engineering for LLMs** | Thomas R. Caldwell | Independent | 2025 | + +### LLM Application Development + +| Title | Author(s) | Publisher | Year | +|:------|:----------|:---------|:-----| +| **AI Engineering: Building Applications with Foundation Models** | Chip Huyen | O'Reilly | 2025 | +| **Build a Large Language Model (From Scratch)** | Sebastian Raschka | Manning | 2024 | +| **Building LLMs for Production** | Louis-FranΓ§ois Bouchard & Louie Peters | O'Reilly | 2024 | +| **LLM Engineer's Handbook** | Paul Iusztin & Maxime Labonne | Packt | 2024 | +| **The Hundred-Page Language Models Book** | Andriy Burkov | Self-Published | 2025 | + +### AI Agents + +| Title | Author(s) | Publisher | Year | +|:------|:----------|:---------|:-----| +| **Building Applications with AI Agents** | Michael Albada | O'Reilly | 2025 | +| **AI Agents and Applications** | Roberto Infante | Manning | 2025 | +| **AI Agents in Action** | Micheal Lanham | Manning | 2025 | + +### Production, Reliability, and Security + +| Title | Author(s) | Publisher | Year | +|:------|:----------|:---------|:-----| +| **LLMs in Production** | Christopher Brousseau & Matthew Sharp | Manning | 2025 | +| **Building Reliable AI Systems** | Rush Shahani | Manning | 2025 | +| **The Developer's Playbook for LLM Security** | Steve Wilson | O'Reilly | 2024 | + +--- + +## Courses +πŸ‘©β€πŸ« + +### Free Short Courses + +- [ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) β€” Co-taught by Andrew Ng and OpenAI's Isa Fulford. The foundational starting point. (DeepLearning.AI) +- [Building Systems with the ChatGPT API](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/) β€” Multi-step LLM system design for production. (DeepLearning.AI) +- [AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/) β€” Agentic dataflows with tool use and research agents. (DeepLearning.AI) +- [Building Agentic RAG with LlamaIndex](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/) β€” RAG research agent construction. (DeepLearning.AI) +- [Functions, Tools and Agents with LangChain](https://www.deeplearning.ai/short-courses/functions-tools-agents-langchain/) β€” Function calling and agent building. (DeepLearning.AI) +- [Prompt Engineering for Vision Models](https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models/) β€” Visual prompting techniques. (DeepLearning.AI) + +### University and Platform Courses + +- [Prompt Engineering Specialization (Vanderbilt)](https://www.coursera.org/specializations/prompt-engineering) β€” 3-course series by Dr. Jules White covering foundational to advanced PE. (Coursera) +- [Generative AI with LLMs (DeepLearning.AI + AWS)](https://www.coursera.org/learn/generative-ai-with-llms) β€” LLM lifecycle, transformers, RLHF, deployment. (Coursera) +- [Stanford CS336: Language Modeling from Scratch](https://cs336.stanford.edu/) β€” Build an LLM end-to-end. (Stanford, 2024–2026) +- [MIT 6.S191: Introduction to Deep Learning](https://introtodeeplearning.com/) β€” Annual course including LLMs and generative AI. (MIT, 2024–2026) +- [The Complete Prompt Engineering for AI Bootcamp](https://www.udemy.com/course/prompt-engineering-for-ai/) β€” Covers GPT-5, DSPy, LangGraph, agent architectures. 58K+ ratings. (Udemy, updated Feb 2026) + +### Free Platform Courses + +- [Google Prompting Essentials](https://grow.google/prompting-essentials/) β€” 5-step prompt design, meta-prompting, Gemini. Under 6 hours. +- [Microsoft Azure AI Fundamentals: Generative AI](https://learn.microsoft.com/en-us/training/paths/introduction-generative-ai/) β€” Free learning path covering LLMs, prompts, agents, Azure OpenAI. +- [Hugging Face LLM Course](https://huggingface.co/learn/llm-course/chapter1/1) β€” Community-driven course covering transformers, fine-tuning, building reasoning models. +- [Hugging Face AI Agents Course](https://huggingface.co/learn) β€” Agent theory to practice. 100K+ registered students. + +### Learn Prompting Courses + +- [ChatGPT for Everyone](https://learnprompting.org/courses/chatgpt-for-everyone) +- [Introduction to Prompt Engineering](https://learnprompting.org/courses/introduction_to_prompt_engineering) +- [Advanced Prompt Engineering](https://learnprompting.org/courses/advanced-prompt-engineering) +- [Introduction to Prompt Hacking](https://learnprompting.org/courses/intro-to-prompt-hacking) +- [Advanced Prompt Hacking](https://learnprompting.org/courses/advanced-prompt-hacking) +- [Introduction to Generative AI Agents for Business Professionals](https://learnprompting.org/courses/introduction-to-agents) +- [AI Safety](https://learnprompting.org/courses/ai-safety) + +--- + +## Tutorials and Guides +πŸ“š + +### Official Provider Guides + +- [OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering) β€” Comprehensive, covering GPT-4.1/5 prompting, reasoning models, structured outputs, agentic workflows. Continuously updated. +- [OpenAI GPT-4.1 Prompting Guide](https://cookbook.openai.com/articles/gpt-4-1-prompting-guide) [2025] β€” Structured agent-like prompt design: goal persistence, tool integration, long-context processing. +- [Anthropic Prompt Engineering Overview](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) β€” Iterative prompt design, XML tags, chain-of-thought, role assignment. Includes prompt generator. +- [Anthropic Claude 4 Best Practices](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/claude-4-best-practices) [2025–2026] β€” Parallel tool execution, thinking capabilities, image processing. +- [Anthropic: Effective Context Engineering for AI Agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) [2025] β€” The evolution from prompt engineering to context engineering: agent state, memory, tools, MCP. +- [Google Gemini Prompting Strategies](https://ai.google.dev/docs/prompt_best_practices) β€” Multimodal prompting for Gemini via Vertex AI and AI Studio. +- [Microsoft Prompt Engineering in Azure AI Studio](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering) β€” Tool calling, function design, few-shot prompting, prompt chaining. + +### Community and Independent Guides + +- [Prompt Engineering Guide (DAIR.AI / promptingguide.ai)](https://www.promptingguide.ai/) β€” Most comprehensive open-source guide. 18+ techniques, model-specific guides, research papers. 3M+ learners. Now includes context engineering. +- [Learn Prompting (learnprompting.org)](https://learnprompting.org/) β€” Structured free platform. Beginner to advanced PE, AI security, HackAPrompt competition. +- [IBM 2026 Guide to Prompt Engineering](https://www.ibm.com/think/prompt-engineering) [2026] β€” Curated tools, tutorials, real-world examples with Python code. +- [Anthropic Interactive Tutorial](https://github.com/anthropics/prompt-eng-interactive-tutorial) β€” 9-chapter Jupyter notebook course with hands-on exercises. +- [Lilian Weng's Prompt Engineering Guide](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/) [2023] β€” Highly respected technical blog from OpenAI researcher. +- [Google Prompt Engineering Guide (68-page PDF)](https://www.reddit.com/r/PromptEngineering/comments/1kggmh0/google_dropped_a_68page_prompt_engineering_guide/) [2025] β€” Internal-style best-practice guide for Gemini with concrete patterns. +- [DigitalOcean: Prompt Engineering Best Practices](https://www.digitalocean.com/resources/articles/prompt-engineering-best-practices) [2025] β€” Updated guide summarizing techniques: few-shot, chain-of-thought, role prompting, etc. +- [Aakash Gupta: Prompt Engineering in 2025](https://news.aakashg.com) [2025] β€” Practical guide with wisdom from shipping AI at OpenAI, Shopify, and Google. +- [Best practices for prompt engineering with OpenAI API](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api) β€” OpenAI's introductory best practices. +- [OpenAI Cookbook](https://github.com/openai/openai-cookbook) β€” Official recipes for function calling, RAG, evaluation, and complex workflows. +- [Microsoft Prompt Engineering Docs](https://microsoft.github.io/prompt-engineering) β€” Microsoft's open prompt engineering resources. +- [DALLE Prompt Book](https://dallery.gallery/the-dalle-2-prompt-book) β€” Visual guide for text-to-image prompting. +- [Best 100+ Stable Diffusion Prompts](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts) β€” Community-curated image generation prompts. +- [Vibe Engineering (Manning)](https://www.manning.com/books/vibe-engineering) β€” Book by Tomasz Lelek & Artur Skowronski on building software through natural language prompts. + +--- + +## Videos +πŸŽ₯ + +- [Andrej Karpathy: "Deep Dive into LLMs" & "How I Use LLMs"](https://www.youtube.com/@AndrejKarpathy) [2024–2025] β€” Two of the most influential AI videos of 2024–2025. Comprehensive technical deep dive followed by practical usage patterns. +- [Karpathy: "Software in the Era of AI" (YC AI Startup School)](https://karpathy.ai/) [2025] β€” Coined "vibe coding" (Feb 2025) and championed "context engineering" (Jun 2025). +- [Karpathy: Neural Networks: Zero to Hero](https://www.youtube.com/@AndrejKarpathy) [2023–2024] β€” Full lecture series building from backpropagation to GPT. +- [3Blue1Brown: Neural Networks Series](https://www.youtube.com/@3blue1brown) [Updated 2024] β€” Iconic animated visual explanations of transformers and attention mechanisms. 7M+ subscribers. +- [AI Explained](https://www.youtube.com/@aiexplained-official) [2024–2025] β€” Long-form analysis breaking down papers, model capabilities, and PE developments. +- [Sam Witteveen](https://www.youtube.com/@samwitteveen) [2024–2025] β€” Practical tutorials on prompt engineering, LangChain, RAG, and agents. +- [Matthew Berman](https://www.youtube.com/@matthew_berman) [2024–2025] β€” Popular channel covering model releases and practical LLM usage. 600K+ subscribers. +- [DeepLearning.AI YouTube](https://www.youtube.com/@Deeplearningai) [2024–2026] β€” Structured lessons, course previews, and Andrew Ng talks on agents and AI careers. +- [Lex Fridman Podcast (AI Episodes)](https://www.youtube.com/@lexfridman) [2024–2025] β€” Long-form interviews with Altman, Hinton, Amodei on LLMs, prompting, and safety. +- [ICSE 2025: AIware Prompt Engineering Tutorial](https://conf.researchr.org/details/icse-2025/icse-2025-tutorials/) [2025] β€” Conference tutorial covering prompt patterns, fragility, anti-patterns, and optimization DSLs. +- [CMU Advanced NLP 2022: Prompting](https://youtube.com/watch?v=5ef83Wljm-M) β€” Foundational academic lecture on prompting methods. +- [ChatGPT: 5 Prompt Engineering Secrets For Beginners](https://www.youtube.com/watch?v=2zg3V66-Fzs) β€” Accessible intro for beginners. + +--- + +## Communities +🀝 + +### Discord Servers + +- [Learn Prompting](https://learnprompting.org/discord) β€” 40,000+ members. Largest PE Discord with courses, hackathons, HackAPrompt competitions. +- [PromptsLab Discord](https://discord.gg/m88xfYMbK6) - Community +- [Midjourney](https://discord.gg/midjourney) β€” 1M+ members. Primary hub for text-to-image prompt sharing. +- [OpenAI Discord](https://discord.gg/openai) β€” Official community with channels for GPTs, Sora, DALL-E, and API help. +- [Anthropic Discord](https://discord.gg/anthropic) β€” Official Claude community for AI development collaboration. +- [Hugging Face Discord](https://discord.gg/huggingface) β€” Model discussions, library support, community events. +- [FlowGPT](https://flowgpt.com/) β€” 33K+ members. 100K+ prompts across ChatGPT, DALL-E, Stable Diffusion, Claude. + +### Reddit + +- [r/PromptEngineering](https://reddit.com/r/PromptEngineering) β€” Dedicated subreddit for prompt crafting techniques and discussions. +- [r/ChatGPT](https://reddit.com/r/ChatGPT) β€” 10M+ members. Primary hub for ChatGPT users and prompt sharing. +- [r/LocalLLaMA](https://reddit.com/r/LocalLLaMA) β€” Highly technical community for running open-source LLMs locally. +- [r/ClaudeAI](https://reddit.com/r/ClaudeAI) β€” Anthropic's Claude community: prompt sharing, API tips, model comparisons. +- [r/MachineLearning](https://reddit.com/r/MachineLearning) β€” Academic-oriented ML research discussions. +- [r/OpenAI](https://reddit.com/r/OpenAI) β€” OpenAI product and API discussions. +- [r/StableDiffusion](https://reddit.com/r/StableDiffusion) β€” 450K+ members for AI art prompting and workflows. +- [r/ChatGPTPromptGenius](https://reddit.com/r/ChatGPTPromptGenius) β€” 35K+ members sharing and refining prompts. + + +### Forums and Platforms + +- [OpenAI Developer Community](https://community.openai.com/) β€” Official forum for API help, best practices, project sharing. +- [Hugging Face Community](https://huggingface.co/) β€” Hub for open-source AI collaboration. +- [DeepLearning.AI Community](https://community.deeplearning.ai/) β€” Forum for learners discussing courses and AI careers. +- [LessWrong](https://www.lesswrong.com/) β€” In-depth technical posts on AI capabilities and safety. +- [AI Alignment Forum](https://www.alignmentforum.org/) β€” Specialized alignment research discussions. +- [CivitAI](https://civitai.com/) β€” Generative AI creators platform for sharing models, LoRAs, and prompts. + +### GitHub Organizations + +- [LangChain](https://github.com/langchain-ai) β€” Open-source LLM app framework. 100K+ stars. +- [Promptslab](https://github.com/promptslab) β€” Generative Models | Prompt-Engineering | LLMs +- [Hugging Face](https://github.com/huggingface) β€” Central hub: Transformers, Diffusers, Datasets, TRL. +- [DSPy (Stanford NLP)](https://github.com/stanfordnlp/dspy) β€” Growing community for systematic prompt optimization. +- [OpenAI](https://github.com/openai) β€” Open-source models, benchmarks, and tools. + +--- + + +## πŸ”¬ Autonomous Research & Self-Improving Agents +> Auto-synced from [awesome-autoresearch](https://github.com/alvinunreal/awesome-autoresearch) Β· Last synced: 2026-06-26 + +### General-Purpose Descendants + +- [kayba-ai/recursive-improve](https://github.com/kayba-ai/recursive-improve) β€” Recursive self-improvement framework where agents capture execution traces, analyze failure patterns, and apply targeted fixes with keep-or-revert evaluation. +- [vukrosic/auto-research](https://github.com/vukrosic/auto-research) β€” Docs-only control plane for an open autonomous AI research lab β€” file-based operating model for human direction and agent execution. +- [uditgoenka/autoresearch](https://github.com/uditgoenka/autoresearch) β€” Claude Code skill that generalizes autoresearch into a reusable loop for software, docs, security, shipping, debugging, and other measurable goals. +- [leo-lilinxiao/codex-autoresearch](https://github.com/leo-lilinxiao/codex-autoresearch) β€” Codex-native autoresearch skill with resume support, lessons across runs, optional parallel experiments, and mode-specific workflows. +- [SeeleAI/Thoth](https://github.com/SeeleAI/Thoth) β€” Dashboard-first Claude Code and Codex runtime for autoresearch, with durable runs, locked work items, visible ledgers, and reviewable verdicts. +- [supratikpm/gemini-autoresearch](https://github.com/supratikpm/gemini-autoresearch) β€” Gemini CLI skill that generalises autoresearch to any measurable goal. Gemini-native: uses Google Search grounding as a live verification source inside the loop, true headless overnight mode via --yolo --prompt, and 1M token context. Also works in Antigravity IDE via .agents/skills/. +- [davebcn87/pi-autoresearch](https://github.com/davebcn87/pi-autoresearch) β€” `pi` extension plus dashboard for persistent experiment loops, live metrics, confidence tracking, and resumable autoresearch sessions. +- [drivelineresearch/autoresearch-claude-code](https://github.com/drivelineresearch/autoresearch-claude-code) β€” Claude Code plugin/skill port of `pi-autoresearch`, with a clean experiment-loop workflow and a concrete biomechanics case study. +- [greyhaven-ai/autocontext](https://github.com/greyhaven-ai/autocontext) β€” Closed-loop control plane for repeated agent improvement, with evaluation, persistent knowledge, staged validation, and optional distillation into cheaper local runtimes. +- [Necmttn/ax](https://github.com/Necmttn/ax) β€” Local retro loop for AI coding agents: captures session traces, turns repeated friction into proposals, and tracks accepted fixes as experiments. +- [jmilinovich/goal-md](https://github.com/jmilinovich/goal-md) β€” Generalizes autoresearch into a `GOAL.md` pattern for repos where the agent must first construct a measurable fitness function before it can optimize. +- [james-s-tayler/lazy-developer](https://github.com/james-s-tayler/lazy-developer) β€” Claude Code skill that orchestrates autoresearch across a prioritized sequence of optimization goals (coverage, test speed, build speed, complexity, LOC, performance) using GOAL.md as the engine. Supports standalone and Ralph Mode multi-instance execution. +- [mutable-state-inc/autoresearch-at-home](https://github.com/mutable-state-inc/autoresearch-at-home) β€” Collaborative fork of upstream autoresearch that adds experiment claiming, shared best-config syncing, hypothesis exchange, and swarm-style coordination across many single-GPU agents. +- [zkarimi22/autoresearch-anything](https://github.com/zkarimi22/autoresearch-anything) β€” Generalizes autoresearch to **any measurable metric** β€” system prompts, API performance, landing pages, test suites, config tuning, SQL queries. "If you can measure it, you can optimize it." +- [Entrpi/autoresearch-everywhere](https://github.com/Entrpi/autoresearch-everywhere) β€” Cross-platform expansion that auto-detects hardware config and starts the loop. The "glue and generalization" half of autoresearch. +- [ShengranHu/ADAS](https://github.com/ShengranHu/ADAS) β€” **Automated Design of Agentic Systems** β€” ICLR 2025. Meta-agents that invent novel agent architectures by programming them in code. +- [MaximeRobeyns/self_improving_coding_agent](https://github.com/MaximeRobeyns/self_improving_coding_agent) β€” **SICA**: Self-Improving Coding Agent that edits its own codebase. ICLR 2025 Workshop paper demonstrating scaffold-level self-improvement on coding benchmarks. +- [peterskoett/self-improving-agent](https://github.com/peterskoett/self-improving-agent) β€” Alternative self-improving agent architecture with reflection and meta-learning cycles. +- [metauto-ai/HGM](https://github.com/metauto-ai/HGM) β€” **Huxley-GΓΆdel Machine** for coding agents β€” applies self-improvement to SWE-bench performance via meta-level optimization. +- [gepa-ai/gepa](https://github.com/gepa-ai/gepa) β€” **GEPA (Genetic-Pareto)** β€” ICLR 2026 Oral. Reflective prompt evolution that outperforms RL (GRPO) on benchmarks. Optimizes any textual parameters against any metric using natural language reflection. +- [sentient-agi/EvoSkill](https://github.com/sentient-agi/EvoSkill) β€” Automated skill discovery for coding agents: evolves reusable skills and prompts from failed trajectories against benchmarks, with support for Claude Code, Codex CLI, OpenCode, OpenHands, and Goose. +- [MrTsepa/autoevolve](https://github.com/MrTsepa/autoevolve) β€” GEPA-inspired autoresearch for self-play: mutate code strategies, evaluate head-to-head, rate with Elo/Bradley-Terry, branch from the Pareto front. Agent reads match traces to target mutations. Works as a Claude Code skill. +- [HKUDS/ClawTeam](https://github.com/HKUDS/ClawTeam) β€” Agent swarm intelligence for autoresearch β€” spawns parallel GPU research directions, distributes work across agents, aggregates results. +- [Orchestra-Research/AI-Research-SKILLs](https://github.com/Orchestra-Research/AI-Research-SKILLs) β€” Comprehensive skill library including autoresearch orchestration with two-loop architecture (inner optimization + outer synthesis). +- [WecoAI/aideml](https://github.com/WecoAI/aideml) β€” **AIDE**: Tree-search ML engineering agent that autonomously improves model performance via iterative code generation and evaluation. +- [weco.ai](https://weco.ai) β€” **Weco**: Cloud platform for AIDE with observability, experiment tracking, and managed runs β€” brings the autoresearch loop into production. + +### Research-Agent Systems + +- [aiming-lab/AutoResearchClaw](https://github.com/aiming-lab/AutoResearchClaw) β€” End-to-end research pipeline that turns a topic into literature review, experiments, analysis, peer review, and paper drafts; broader than autoresearch, but clearly in the same lineage. +- [OpenRaiser/NanoResearch](https://github.com/OpenRaiser/NanoResearch) β€” End-to-end autonomous research engine that plans experiments, generates code, runs jobs locally or on SLURM, analyzes real results, and writes papers grounded in those outputs. +- [kaust-ark/ARK](https://github.com/kaust-ark/ARK) β€” **ARK (Automatic Research Kit)**: idea + venue β†’ paper pipeline orchestrating 6 agents β€” proposal analysis, literature search, Slurm experiments, LaTeX drafting, iterative peer review. Controlled via CLI, web dashboard, or Telegram. +- [wanshuiyin/Auto-claude-code-research-in-sleep](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep) β€” Markdown-first research workflows for Claude Code and other agents, centered on autonomous literature review, experiments, paper iteration, and cross-model critique. +- [skyllwt/AutoSci](https://github.com/skyllwt/AutoSci) β€” Wiki-centric full-lifecycle research platform built on Claude Code, realizing Karpathy's LLM-Wiki vision. 20+ skills cover the full loop: ingest β†’ ideate β†’ novelty check β†’ experiment design / run / eval β†’ paper writing. Research state lives in a structured knowledge wiki with an interactive graph. +- [Sibyl-Research-Team/AutoResearch-SibylSystem](https://github.com/Sibyl-Research-Team/AutoResearch-SibylSystem) β€” Fully autonomous AI scientist built on Claude Code, with explicit AutoResearch lineage, multi-agent research iteration, GPU experiment execution, and a self-evolving outer loop. +- [eimenhmdt/autoresearcher](https://github.com/eimenhmdt/autoresearcher) β€” Early open-source package for automating scientific workflows, currently centered on literature-review generation with an ambition toward broader autonomous research. +- [hyperspaceai/agi](https://github.com/hyperspaceai/agi) β€” Distributed, peer-to-peer research network where autonomous agents run experiments, gossip findings, maintain CRDT leaderboards, and archive results to GitHub across multiple research domains. +- [Human-Agent-Society/CORAL](https://github.com/Human-Agent-Society/CORAL) β€” **CORAL**: Autonomous multi-agent evolution for open-ended discovery ([arXiv:2604.01658](https://arxiv.org/abs/2604.01658)). Long-running agents with shared persistent memory, asynchronous execution, and heartbeat-based interventions; SOTA on 10 math/algorithmic/systems tasks. +- [SakanaAI/AI-Scientist](https://github.com/SakanaAI/AI-Scientist) β€” **The AI Scientist**: First comprehensive system for fully automatic scientific discovery. From idea generation to paper writing with minimal human supervision. +- [SakanaAI/AI-Scientist-v2](https://github.com/SakanaAI/AI-Scientist-v2) β€” Workshop-level automated scientific discovery via agentic tree search. Removes template dependency from v1, generalizes across research domains. +- [AweAI-Team/AiScientist](https://github.com/AweAI-Team/AiScientist) β€” **AiScientist**: long-horizon ML research lab with hierarchical orchestration and File-as-Bus coordination β€” workspace files act as the durable system of record. Drives autonomous paper-reproduction (PaperBench) and competition-style MLE-Bench iteration loops under fixed compute/time budgets. ([arXiv 2604.13018](https://arxiv.org/abs/2604.13018)) +- [HKUDS/AI-Researcher](https://github.com/HKUDS/AI-Researcher) β€” NeurIPS 2025 paper. Full end-to-end research automation: hypothesis β†’ experiments β†’ manuscript β†’ peer review. Production version at [novix.science](https://novix.science/chat). +- [openags/Auto-Research](https://github.com/openags/Auto-Research) β€” **OpenAGS**: Orchestrates a team of AI agents across the full research lifecycle β€” lit review, hypothesis generation, experiments, manuscript writing, and peer review. +- [SamuelSchmidgall/AgentLaboratory](https://github.com/SamuelSchmidgall/AgentLaboratory) β€” End-to-end autonomous research workflow: idea β†’ literature review β†’ experiments β†’ report. Supports both autonomous and co-pilot modes. +- [AgentRxiv](https://agentrxiv.github.io/) β€” Collaborative autonomous research framework where agent laboratories share a preprint server to build on each other's work iteratively. +- [JinheonBaek/ResearchAgent](https://github.com/JinheonBaek/ResearchAgent) β€” Iterative research idea generation over scientific literature with LLMs. Multi-agent review and feedback loops. +- [du-nlp-lab/MLR-Copilot](https://github.com/du-nlp-lab/MLR-Copilot) β€” Autonomous ML research framework β€” generates ideas, implements experiments, analyzes results. +- [MASWorks/ML-Agent](https://github.com/MASWorks/ML-Agent) β€” Reinforcing LLM agents for autonomous ML engineering. Learns from trial and error to improve model performance. +- [PouriaRouzrokh/LatteReview](https://github.com/PouriaRouzrokh/LatteReview) β€” Low-code Python package for **automated systematic literature reviews** via AI-powered agents. +- [LitLLM/LitLLM](https://github.com/LitLLM/LitLLM) β€” AI-powered literature review assistant using RAG for accurate, well-structured related-work sections in academic writing. +- [Agent Laboratory](https://agentlaboratory.github.io/) β€” Three-phase research pipeline: Literature Review β†’ Experimentation β†’ Report Writing, with specialized agents for each phase. + +### Platform Ports & Hardware Forks + +- [gianfrancopiana/openclaw-autoresearch](https://github.com/gianfrancopiana/openclaw-autoresearch) β€” OpenClaw port of pi-autoresearch; autonomous experiment loop for any optimization target with statistical confidence scoring. +- [miolini/autoresearch-macos](https://github.com/miolini/autoresearch-macos) β€” Widely adopted macOS fork that adapts upstream autoresearch for Apple Silicon / MPS while preserving the original loop shape. +- [trevin-creator/autoresearch-mlx](https://github.com/trevin-creator/autoresearch-mlx) β€” MLX-native Apple Silicon port that keeps the upstream fixed-budget `val_bpb` loop while removing the PyTorch/CUDA dependency entirely. +- [jsegov/autoresearch-win-rtx](https://github.com/jsegov/autoresearch-win-rtx) β€” Windows-native RTX fork focused on consumer NVIDIA GPUs, with explicit VRAM floors and a practical desktop setup path. +- [iii-hq/n-autoresearch](https://github.com/iii-hq/n-autoresearch) β€” Multi-GPU autoresearch infrastructure with structured experiment tracking, adaptive search strategy, crash recovery, and queryable orchestration around the classic `train.py` loop. +- [lucasgelfond/autoresearch-webgpu](https://github.com/lucasgelfond/autoresearch-webgpu) β€” Browser/WebGPU port that lets agents generate training code, run experiments in-browser, and feed results back into the loop without a Python setup. +- [tonitangpotato/autoresearch-engram](https://github.com/tonitangpotato/autoresearch-engram) β€” Fork with **persistent cognitive memory** β€” frequency-weighted retrieval of cross-session knowledge for improved experiment continuity. +- [Colab/Kaggle T4 port](https://github.com/karpathy/autoresearch/issues/208) β€” Adapts autoresearch for free T4 GPUs (Google Colab / Kaggle) with zero cost and zero local setup. Key changes: Flash Attention 3 β†’ PyTorch SDPA, removes H100-only kernel dependency. +- [ArmanJR-Lab/autoautoresearch](https://github.com/ArmanJR-Lab/autoautoresearch) β€” Jetson AGX Orin port with a **director** β€” a Go binary that acts as a "creative director" injecting novelty (arxiv papers + DeepSeek Reasoner) into the loop to escape local minima. Includes multi-experiment comparison (baseline vs director-guided) with detailed stall analysis. + +### Domain-Specific Adaptations + +- [mattprusak/autoresearch-genealogy](https://github.com/mattprusak/autoresearch-genealogy) β€” Applies the autoresearch pattern to genealogy, using structured prompts, archive guides, source checks, and vault workflows to iteratively expand and verify family-history research. +- [ArchishmanSengupta/autovoiceevals](https://github.com/ArchishmanSengupta/autovoiceevals) β€” Uses adversarial callers plus keep-or-revert prompt edits to harden voice AI agents across Vapi, Smallest AI, and ElevenLabs. +- [chrisworsey55/atlas-gic](https://github.com/chrisworsey55/atlas-gic) β€” Applies the autoresearch keep-or-revert loop to trading agents, optimizing prompts and portfolio orchestration against rolling Sharpe ratio instead of model loss. +- [RightNow-AI/autokernel](https://github.com/RightNow-AI/autokernel) β€” Applies the autoresearch loop to GPU kernel optimization: profile bottlenecks, edit one kernel, benchmark, keep or revert, repeat. +- [ElliotXie/autozyme](https://github.com/ElliotXie/autozyme) β€” Multi-agent framework that applies the autoresearch keep-or-revert loop to CPU-side scientific software: profile a target function, generate one optimization candidate, benchmark for speed while preserving the original outputs, keep or revert, repeat. +- [Agent-Analytics/autoresearch-growth](https://github.com/Agent-Analytics/autoresearch-growth) β€” Applies autoresearch to landing-page positioning and A/B test candidates, using analytics snapshots and measured experiment results to seed subsequent rounds. +- [Rkcr7/autoresearch-sudoku](https://github.com/Rkcr7/autoresearch-sudoku) β€” Enhanced autoresearch workflow where an AI agent iteratively rewrites and benchmarks a Rust sudoku solver, ultimately beating leading human-built solvers on hard benchmark sets. +- [jeongph/autospec](https://github.com/jeongph/autospec) β€” Reads natural-language business rules and autonomously builds a Spring Boot service with tests via the keep-or-revert loop. Evaluates with Gradle build + JUnit XML. 119-line skeleton to 950 lines in 5 cycles. +- [vlasenkoalexey/tpu_performance_autoresearch_wiki](https://github.com/vlasenkoalexey/tpu_performance_autoresearch_wiki) β€” Applies the autoresearch keep-or-revert loop to TPU model performance (MFU / tokens-per-sec) on v6e hardware: profiles each run through an XProf MCP server, makes one model-code change per experiment, and keeps or reverts against measured MFU. Pairs the loop with a Karpathy-style LLM wiki for domain knowledge and per-experiment optimization traces; includes Llama3-8B and Qwen3-8B case studies across JAX and torchax lanes. + +### Evaluation & Benchmarks + +- [snap-stanford/MLAgentBench](https://github.com/snap-stanford/MLAgentBench) β€” Benchmark suite for evaluating AI agents on ML experimentation tasks. 13 tasks from CIFAR-10 to BabyLM. +- [OpenAI/mle-bench](https://github.com/openai/mle-bench) β€” OpenAI's benchmark for measuring how well AI agents perform at ML engineering. +- [chchenhui/mlrbench](https://github.com/chchenhui/mlrbench) β€” MLR-Bench: Evaluating AI agents on open-ended ML research. 201 tasks from NeurIPS/ICLR/ICML workshops. +- [gersteinlab/ML-Bench](https://github.com/gersteinlab/ML-Bench) β€” Evaluates LLMs and agents for ML tasks on repository-level code. +- [THUDM/AgentBench](https://github.com/THUDM/AgentBench) β€” Comprehensive benchmark for LLM-as-Agent evaluation across 8 distinct environments. ICLR 2024. + +### Related Resources + +- [ai-agents-2030/awesome-deep-research-agent](https://github.com/ai-agents-2030/awesome-deep-research-agent) β€” Curated list of deep research agent papers and systems. +- [YoungDubbyDu/LLM-Agent-Optimization](https://github.com/YoungDubbyDu/LLM-Agent-Optimization) β€” Papers on LLM agent optimization methods. +- [VoltAgent/awesome-ai-agent-papers](https://github.com/VoltAgent/awesome-ai-agent-papers) β€” Curated AI agent papers from 2026 β€” agent engineering, memory, evaluation, workflows, and autonomous systems. +- [masamasa59/ai-agent-papers](https://github.com/masamasa59/ai-agent-papers) β€” AI agent research papers updated biweekly via automated arxiv search with curated selection. +- [tmgthb/Autonomous-Agents](https://github.com/tmgthb/Autonomous-Agents) β€” Autonomous agents research papers, updated daily. +- [HKUST-KnowComp/Awesome-LLM-Scientific-Discovery](https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery) β€” EMNLP 2025 survey on LLMs in scientific discovery. +- [openags/Awesome-AI-Scientist-Papers](https://github.com/openags/Awesome-AI-Scientist-Papers) β€” Collection of AI Scientist / Robot Scientist papers. +- [agenticscience.github.io](https://agenticscience.github.io/) β€” Survey: "From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery." +- [dspy.ai/GEPA](https://dspy.ai/api/optimizers/GEPA/overview/) β€” DSPy integration of GEPA reflective prompt optimizer for compound AI systems. +- [OpenAI Cookbook: Self-Evolving Agents](https://developers.openai.com/cookbook/examples/partners/self_evolving_agents/autonomous_agent_retraining) β€” Cookbook for autonomous agent retraining using GEPA-style reflective evolution. +- [WecoAI/awesome-autoresearch](https://github.com/WecoAI/awesome-autoresearch) β€” Curated list of AutoResearch use cases with verifiable traces and progress charts, organized by domain (LLM training, GPU kernels, voice agents, trading, etc.). + + + +--- + +## How to Contribute + +We welcome contributions to this list! Before contributing, please take a moment to review our [contribution guidelines](contributing.md). These guidelines will help ensure that your contributions align with our objectives and meet our standards for quality and relevance. + +**What we're looking for:** +- New high-quality papers, tools, or resources with a brief description of why they matter +- Updates to existing entries (broken links, outdated information) +- Corrections to star counts, pricing, or model details +- Translations and accessibility improvements + +**Quality standards:** +- All tools should be actively maintained (updated within the last 6 months) +- Papers should be from peer-reviewed venues or have significant community adoption +- Datasets should be publicly accessible +- Please include a one-line description explaining why the resource is valuable + +Thank you for your interest in contributing to this project! + + + + + +--- + +

+ Maintained by PromptsLab Β· Star this repo if you find it useful! +

-- 2.54.0 From 4b6477b78c32b19edd42a2261b6ca1566833a05f Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:42 +0000 Subject: [PATCH 04/10] [upstream-sync] _source/papers.json from promptslab/awesome-prompt-engineering@3b022d69 [catalogue] --- .../catalogue/_source/papers.json | 345 ++++++++++++++++++ 1 file changed, 345 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/_source/papers.json diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/_source/papers.json b/upstream/promptslab-awesome-prompt-engineering/catalogue/_source/papers.json new file mode 100644 index 0000000..9f66032 --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/_source/papers.json @@ -0,0 +1,345 @@ +--- +title: "Papers" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/_source/papers.json +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +[ + { + "p":"Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery", + "i":"https://arxiv.org/abs/2302.03668", + "y":2023, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Multimodal Chain-of-Thought Reasoning in Language Models", + "i":"https://arxiv.org/abs/2302.00923", + "y":2023, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Large Language Models Can Be Easily Distracted by Irrelevant Context", + "i":"https://arxiv.org/abs/2302.00093", + "y":2023, + "cc":"Evaluating and Improving Language Models" + }, + { + "p":"Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models", + "i":"https://arxiv.org/abs/2302.00618", + "y":2023, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Progressive Prompts: Continual Learning for Language Models", + "i":"https://arxiv.org/abs/2301.12314", + "y":2023, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Batch Prompting: Efficient Inference with LLM APIs", + "i":"https://arxiv.org/abs/2301.08721", + "y":2023, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Prompting for Multimodal Hateful Meme Classification", + "i":"https://arxiv.org/abs/2302.04156", + "y":2023, + "cc":"Applications of Language Models" + }, + { + "p":"PLACES: Prompting Language Models for Social Conversation Synthesis", + "i":"https://arxiv.org/abs/2302.03269", + "y":2023, + "cc":"Applications of Language Models" + }, + { + "p":"Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation", + "i":"https://arxiv.org/abs/2302.01441", + "y":2023, + "cc":"Applications of Language Models" + }, + { + "p":"Crawling the Internal Knowledge-Base of Language Models", + "i":"https://arxiv.org/abs/2301.12810", + "y":2023, + "cc":"Evaluating and Improving Language Models" + }, + { + "p":"On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning", + "i":"https://arxiv.org/abs/2212.08061", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Constitutional AI: Harmlessness from AI Feedback", + "i":"https://arxiv.org/abs/2212.08073", + "y":2022, + "cc":"Threat Detection and Adversarial Examples" + }, + { + "p":"Successive Prompting for Decompleting Complex Questions", + "i":"https://arxiv.org/abs/2212.04092", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Discovering Language Model Behaviors with Model-Written Evaluations", + "i":"https://arxiv.org/abs/2212.09251", + "y":2022, + "cc":"Evaluating and Improving Language Models" + }, + { + "p":"Structured Prompting: Scaling In-Context Learning to 1,000 Examples", + "i":"https://arxiv.org/abs/2212.06713", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"PAL: Program-aided Language Models", + "i":"https://arxiv.org/abs/2211.10435", + "y":2022, + "cc":"Applications of Language Models" + }, + { + "p":"Large Language Models Are Human-Level Prompt Engineers", + "i":"https://arxiv.org/abs/2211.01910", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Ignore Previous Prompt: Attack Techniques For Language Models", + "i":"https://arxiv.org/abs/2211.09527", + "y":2022, + "cc":"Threat Detection and Adversarial Examples" + }, + { + "p":"Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods", + "i":"https://arxiv.org/abs/2210.07321", + "y":2022, + "cc":"Threat Detection and Adversarial Examples" + }, + { + "p":"Ask Me Anything: A simple strategy for prompting language models", + "i":"https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"ReAct: Synergizing Reasoning and Acting in Language Models", + "i":"https://arxiv.org/abs/2210.03629", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Prompting GPT-3 To Be Reliable", + "i":"https://arxiv.org/abs/2210.09150", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Decomposed Prompting: A Modular Approach for Solving Complex Tasks", + "i":"https://arxiv.org/abs/2210.02406", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought", + "i":"https://arxiv.org/abs/2210.01240v3", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples", + "i":"https://arxiv.org/abs/2209.02128", + "y":2022, + "cc":"Threat Detection and Adversarial Examples" + }, + { + "p":"Promptagator: Few-shot Dense Retrieval From 8 Examples", + "i":"https://arxiv.org/abs/2209.11755", + "y":2022, + "cc":"Few-shot Learning and Performance Optimization" + }, + { + "p":"On the Advance of Making Language Models Better Reasoners", + "i":"https://arxiv.org/abs/2206.02336", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Large Language Models are Zero-Shot Reasoners", + "i":"https://arxiv.org/abs/2205.11916", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Toxicity Detection with Generative Prompt-based Inference", + "i":"https://arxiv.org/abs/2205.12390", + "y":2022, + "cc":"Threat Detection and Adversarial Examples" + }, + { + "p":"The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning", + "i":"https://arxiv.org/abs/2205.03401", + "y":2022, + "cc":"Few-shot Learning and Performance Optimization" + }, + { + "p":"A Taxonomy of Prompt Modifiers for Text-To-Image Generation", + "i":"https://arxiv.org/abs/2204.13988", + "y":2022, + "cc":"Text-to-Image Generation" + }, + { + "p":"PromptChainer: Chaining Large Language Model Prompts through Visual Programming", + "i":"https://arxiv.org/abs/2203.06566", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Self-Consistency Improves Chain of Thought Reasoning in Language Models", + "i":"https://arxiv.org/abs/2203.11171", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?", + "i":"https://arxiv.org/abs/2202.12837", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Legal Prompt Engineering for Multilingual Legal Judgement Prediction", + "i":"https://arxiv.org/abs/2212.02199", + "y":2022, + "cc":"Applications of Language Models" + }, + { + "p":"Investigating Prompt Engineering in Diffusion Models", + "i":"https://arxiv.org/abs/2211.15462", + "y":2022, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering", + "i":"https://arxiv.org/abs/2209.09513v2", + "y":2022, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language", + "i":"https://arxiv.org/abs/2210.15157", + "y":2022, + "cc":"Applications of Language Models" + }, + { + "p":"Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?", + "i":"https://arxiv.org/abs/2210.14699", + "y":2022, + "cc":"Overviews" + }, + { + "p":"Plot Writing From Scratch Pre-Trained Language Models", + "i":"https://aclanthology.org/2022.inlg-main.5", + "y":2022, + "cc":"Applications of Language Models" + }, + { + "p":"Chain of Thought Prompting Elicits Reasoning in Large Language Models", + "i":"https://arxiv.org/abs/2201.11903", + "y":2021, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Show Your Work: Scratchpads for Intermediate Computation with Language Models", + "i":"https://arxiv.org/abs/2112.00114", + "y":2021, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Generated Knowledge Prompting for Commonsense Reasoning", + "i":"https://arxiv.org/abs/2110.08387", + "y":2021, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"Reframing Instructional Prompts to GPTk's Language", + "i":"https://arxiv.org/abs/2109.07830", + "y":2021, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Design Guidelines for Prompt Engineering Text-to-Image Generative Models", + "i":"https://arxiv.org/abs/2109.06977", + "y":2021, + "cc":"Text-to-Image Generation" + }, + { + "p":"Making Pre-trained Language Models Better Few-shot Learners", + "i":"https://aclanthology.org/2021.acl-long.295", + "y":2021, + "cc":"Few-shot Learning and Performance Optimization" + }, + { + "p":"Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity", + "i":"https://arxiv.org/abs/2104.08786", + "y":2021, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"BERTese: Learning to Speak to BERT", + "i":"https://aclanthology.org/2021.eacl-main.316", + "y":2021, + "cc":"Reasoning and In-Context Learning" + }, + { + "p":"The Power of Scale for Parameter-Efficient Prompt Tuning", + "i":"https://arxiv.org/abs/2104.08691", + "y":2021, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm", + "i":"https://arxiv.org/abs/2102.07350", + "y":2021, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"Calibrate Before Use: Improving Few-Shot Performance of Language Models", + "i":"https://arxiv.org/abs/2102.09690", + "y":2021, + "cc":"Evaluating and Improving Language Models" + }, + { + "p":"Prefix-Tuning: Optimizing Continuous Prompts for Generation", + "i":"https://arxiv.org/abs/2101.00190", + "y":2021, + "cc":"Prompt Engineering Techniques" + }, + { + "p":"AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts", + "i":"https://arxiv.org/abs/2010.15980", + "y":2020, + "cc":"Applications of Language Models" + }, + { + "p":"Language Models are Few-Shot Learners", + "i":"https://arxiv.org/abs/2005.14165", + "y":2020, + "cc":"Few-shot Learning and Performance Optimization" + }, + { + "p":"How Can We Know What Language Models Know?", + "i":"https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/How-Can-We-Know-What-Language-Models-Know", + "y":2020, + "cc":"Threat Detection and Adversarial Examples" + } +] -- 2.54.0 From 14e43b4763e297d97297a46e6b18cfe55c299258 Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:46 +0000 Subject: [PATCH 05/10] [upstream-sync] code-of-conduct.md from promptslab/awesome-prompt-engineering@3b022d69 [unknown] --- .../catalogue/code-of-conduct.md | 141 ++++++++++++++++++ 1 file changed, 141 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/code-of-conduct.md diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/code-of-conduct.md b/upstream/promptslab-awesome-prompt-engineering/catalogue/code-of-conduct.md new file mode 100644 index 0000000..9a70a97 --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/code-of-conduct.md @@ -0,0 +1,141 @@ +--- +title: "Contributor Covenant Code of Conduct" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/code-of-conduct.md +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. -- 2.54.0 From 68ddd34422bfd6e303751f447b42e454a3d30ab9 Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:50 +0000 Subject: [PATCH 06/10] [upstream-sync] contributing.md from promptslab/awesome-prompt-engineering@3b022d69 [catalogue] --- .../catalogue/contributing.md | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/contributing.md diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/contributing.md b/upstream/promptslab-awesome-prompt-engineering/catalogue/contributing.md new file mode 100644 index 0000000..5e07afe --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/contributing.md @@ -0,0 +1,27 @@ +--- +title: "Contribution Guidelines:" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/contributing.md +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +# Contribution Guidelines: + +Thank you for considering contributing to our Github project. To ensure a smooth and efficient contribution process, please adhere to the following guidelines: + +- **Test your tools:** + - Before submitting a pull request, make sure to test the tools yourself. This ensures that they are functional and free from any errors or bugs. +- **Ensure tool quality:** + - Only link to tools that are of high quality and meet our project standards. + +## Updating your PR + +If the maintainers notice any changes that are required in your pull request, we will ask you to update it before merging. Instead of opening a new PR, simply edit the existing one. + +Thank you for your suggestions and contributions! -- 2.54.0 From 8123d8db9f8479454b0615a14967307aa83f3d3c Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:53 +0000 Subject: [PATCH 07/10] [upstream-sync] website/README.md from promptslab/awesome-prompt-engineering@3b022d69 [catalogue] --- .../catalogue/website/README.md | 35 +++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/website/README.md diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/website/README.md b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/README.md new file mode 100644 index 0000000..1670d5a --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/README.md @@ -0,0 +1,35 @@ +--- +title: "Readme" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/website/README.md +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +## Awesome Prompt Engineering β€” Website + +This is the website for the [Awesome Prompt Engineering](https://github.com/promptslab/Awesome-Prompt-Engineering) collection. + +### How it works + +- Built with Next.js (App Router) + Tailwind CSS +- Fetches README.md from GitHub at runtime (ISR, 5-minute revalidation) +- Parses markdown into structured typed data +- No manual data sync needed β€” README is the single source of truth + +### Development + +```bash +cd website +npm install +npm run dev +``` + +### Deploy + +Set Vercel root directory to `website/`. No additional config needed. -- 2.54.0 From e5f12f0cce2ca89dd968165aa19ce4542aa39b9a Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:21:59 +0000 Subject: [PATCH 08/10] [upstream-sync] website/package-lock.json from promptslab/awesome-prompt-engineering@3b022d69 [catalogue] --- .../catalogue/website/package-lock.json | 5790 +++++++++++++++++ 1 file changed, 5790 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/website/package-lock.json diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/website/package-lock.json b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/package-lock.json new file mode 100644 index 0000000..025f45f --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/package-lock.json @@ -0,0 +1,5790 @@ +--- +title: "Package Lock" +task: "" +lineage_type: import +upstream_source: 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.../catalogue/website/package.json | 42 +++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/website/package.json diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/website/package.json b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/package.json new file mode 100644 index 0000000..3d4ba3e --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/package.json @@ -0,0 +1,42 @@ +--- +title: "Package" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/website/package.json +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +{ + "name": "website", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "eslint" + }, + "dependencies": { + "fuse.js": "^7.1.0", + "next": "15.5.12", + "react": "19.1.0", + "react-dom": "19.1.0", + "resend": "^6.9.2" + }, + "devDependencies": { + "@eslint/eslintrc": "^3", + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "15.5.12", + "tailwindcss": "^4", + "typescript": "^5" + } +} -- 2.54.0 From ccd2f59a167cce0e71c61fe070293cf4e8997940 Mon Sep 17 00:00:00 2001 From: promptadmin Date: Fri, 26 Jun 2026 17:22:14 +0000 Subject: [PATCH 10/10] [upstream-sync] website/tsconfig.json from promptslab/awesome-prompt-engineering@3b022d69 [unknown] --- .../catalogue/website/tsconfig.json | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 upstream/promptslab-awesome-prompt-engineering/catalogue/website/tsconfig.json diff --git a/upstream/promptslab-awesome-prompt-engineering/catalogue/website/tsconfig.json b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/tsconfig.json new file mode 100644 index 0000000..61ab0a7 --- /dev/null +++ b/upstream/promptslab-awesome-prompt-engineering/catalogue/website/tsconfig.json @@ -0,0 +1,40 @@ +--- +title: "Tsconfig" +task: "" +lineage_type: import +upstream_source: https://github.com/promptslab/awesome-prompt-engineering/blob/3b022d69/website/tsconfig.json +upstream_sha: 3b022d69 +imported_at: 2026-06-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +{ + "compilerOptions": { + "target": "ES2017", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./src/*"] + } + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], + "exclude": ["node_modules"] +} -- 2.54.0