50 USEFUL GITHUB REPOS

1. iFixAi — AI misalignment testing → https://github.com/ifixai-ai/iFixAI…

2. public-apis — free APIs for everything → https://github.com/public-apis/public-apis…

3. build-your-own-x — learn by building → https://github.com/codecrafters-io/build-your-own-x…

4. developer-roadmap — learn any tech skill → https://github.com/kamranahmedse/developer-roadmap…

5. free-programming-books — thousands of free books → https://github.com/EbookFoundation/free-programming-books…

6. system-design-primer → https://github.com/donnemartin/system-design-primer…

7. coding-interview-university → https://github.com/jwasham/coding-interview-university…

8. the-art-of-command-line → https://github.com/jlevy/the-art-of-command-line…

9. project-based-learning → https://github.com/practical-tutorials/project-based-learning…

10. you-dont-know-js → https://github.com/getify/You-Dont-Know-JS…

11. the-book-of-secret-knowledge → https://github.com/trimstray/the-book-of-secret-knowledge…

12. tech-interview-handbook → https://github.com/yangshun/tech-interview-handbook…

13. awesome-selfhosted → https://github.com/awesome-selfhosted/awesome-selfhosted…

14. javascript-algorithms → https://github.com/trekhleb/javascript-algorithms…

15. 30-seconds-of-code → https://github.com/Chalarangelo/30-seconds-of-code…

16. github gitignore templates → https://github.com/github/gitignore…

17. ollama — run LLMs locally → https://github.com/ollama/ollama

18. langchain → https://github.com/langchain-ai/langchain…

19. n8n automation → https://github.com/n8n-io/n8n

20. openclaw — local AI assistant → https://github.com/openclaw/openclaw…

21. dify — AI app builder → https://github.com/langgenius/dify

22. langflow → https://github.com/langflow-ai/langflow…

23. mem0 — AI memory layer → https://github.com/mem0ai/mem0

24. browser-use → https://github.com/browser-use/browser-use…

25. crewAI → https://github.com/crewAIInc/crewAI…

26. MetaGPT → https://github.com/geekan/MetaGPT

27. AutoGen (Microsoft) → https://github.com/microsoft/autogen…

28. aider — AI coding assistant → https://github.com/Aider-AI/aider

29. markitdown (Microsoft) → https://github.com/microsoft/markitdown…

30. open-webui → https://github.com/open-webui/open-webui…

31. maigret — OSINT tool → https://github.com/soxoj/maigret

32. TradingAgents → https://github.com/TauricResearch/TradingAgents…

33. stagehand → https://github.com/browserbase/stagehand…

34. firecrawl → https://github.com/mendableai/firecrawl…

35. transformers (HuggingFace) → https://github.com/huggingface/transformers…

36. vLLM → https://github.com/vllm-project/vllm…

37. llama.cpp → https://github.com/ggerganov/llama.cpp…

38. llama_index → https://github.com/run-llama/llama_index…

39. nanoGPT (Karpathy) → https://github.com/karpathy/nanoGPT…

40. RAGFlow → https://github.com/infiniflow/ragflow…

41. supermemory → https://github.com/supermemoryai/supermemory…

42. awesome-claude-skills → https://github.com/ComposioHQ/awesome-claude-skills…

43. Bumblebee (Perplexity AI security tool) → https://github.com/perplexityai/bumblebee…

44. ComfyUI → https://github.com/comfyanonymous/ComfyUI…

45. DeepSeek (official repo varies by model release) → https://github.com/deepseek-ai

46. Lobe Chat → https://github.com/lobehub/lobe-chat…

47. freeCodeCamp → https://github.com/freeCodeCamp/freeCodeCamp…

48. system design + interview prep extras (bonus) → https://github.com/jwasham/coding-interview-university…

49. AI agents ecosystem (extra) → https://github.com/langchain-ai/langchain…

50. automation + AI workflows (extra) → https://github.com/n8n-io/n8n

Save this before you forget it exists.

You’ll come back to at least ten of these.

ifixai-ai/iFixAI

Source: https://github.com/ifixai-ai/iFixAI

iFixAi

The diagnostic for AI operational misalignment

Catch your agent's mistakes and blind spots before the shit hits the fan.

Quick start • Three ways to run • Test your agent • Scoring • Docs • Contributing

One ifixai run, end to end: guided setup picks the system, judge, and suite; the run verifies the connection and saves your config; 32 inspections execute across five pillars; and the result lands as an A–F grade with a scored core-pillar scorecard.

What it is

iFixAi detects AI operational misalignment before it damages your business. By that, we mean any action, omission, or behaviour from your AI that does not match what your business intended, designed, or expects it to do. The dangerous part is that this rarely shows up in your usual KPIs. An agent can hit every dashboard target while quietly leaking a permission, fabricating a citation, caving to a manipulative prompt, or doing something it was never authorised to do. Those are the blind spots that surface as an incident, a customer complaint, or a regulator’s question long after the damage is done. iFixAi finds them first.

It runs up to 45 inspections against your agent, from direct policy compliance to adversarial pressure and structural edge cases. These come in two tiers: 32 core plus 13 extended. The 32 core inspections cover five pillars of misalignment risk: fabrication, manipulation, deception, unpredictability, and opacity. Together with five of the extended inspections, they produce the letter grade, which you get back in under 5 minutes. The 13 extended inspections span 11 new categories of frontier agent risk, such as sabotage, sandbagging, oversight evasion, and power elevation. Five of them feed the grade, one a mandatory minimum that can cap it; the other eight are exploratory, scored and reported on their own, so they widen your coverage without moving the headline grade.

Because the whole point is trust, iFixAi is honest about what it is. It is not a certification or a safety guarantee. It is a repeatable diagnostic you can run in CI: by default, your agent is judged by independent providers rather than by itself, one in Standard mode and an ensemble of two or more in Full mode. Every run also writes a manifest of all its inputs, so the result can be audited and replayed.

Three ways to run

All three run the same diagnostic underneath. The difference is how you configure and drive it.

Quick start

Now try it yourself. The guided wizard gets you running with zero flags from the second run onward; the Claude Code plugin lets Claude drive the whole thing; or use explicit flags for full control and CI. Full walkthrough: docs/get-started.md.

Guided wizard (recommended)

pip install "ifixai[openai]"   # or anthropic, gemini, etc. — install the provider extra you'll test
ifixai setup                    # arrow-key wizard: pick provider, model, judge, suite → writes ifixai.yaml
ifixai run                      # no flags needed from now on

ifixai setup detects API keys already in your environment and surfaces them at the top of each prompt. No key found? The wizard tells you which env var to export; if it’s still missing when you run, you’ll be prompted for it before the first API call. After setup, ifixai run reads everything from ifixai.yaml — no flags, no copy-pasting keys.

Claude Code plugin

Let Claude run the whole thing for you, with no flags or fixtures to write. If you already have Claude Code, from inside it:

1. Install it. Add this repo as a plugin marketplace, then install the plugin:

   /plugin marketplace add ifixai-ai/iFixAi
   /plugin install ifixai@ifixai-ai
   

   Restart Claude Code or run /reload-plugins if it doesn’t show up right away.

2. Run it. Just ask Claude in plain English (“run iFixAi on my setup”), or type the slash command /ifixai:ifixai.

Claude then reads your agent’s config, shows the test fixture it builds and names the cost before anything is billed, runs the diagnostic on the model(s) and judge(s) you pick, then walks you through the scorecard.

Explicit flags

# 1. Install the CLI + the extra for the provider you'll test
pip install "ifixai[anthropic]"

# 2. Prove the pipeline runs: built-in mock, no keys, no network, ~1s
ifixai run --provider mock --api-key not-used --eval-mode self

# 3. Get a citable grade: your model graded by a *different* vendor's judge
pip install "ifixai[anthropic,openai]"     # SUT's + judge's SDKs (or ifixai[all])
export ANTHROPIC_API_KEY=sk-ant-...         # the SUT, graded
export OPENAI_API_KEY=sk-...                # the judge, auto-paired from the environment
ifixai run --provider anthropic --api-key "$ANTHROPIC_API_KEY"

Every run has two roles, and a citable run needs a key for each:

Reports land in ./ifixai-results/ as JSON and Markdown. Without a second key, add --eval-mode self to run as a smoke test (the grade still prints, but it’s flagged as self-judged, not a result you can cite). Pinning the judge, Full-mode ensembles, and the eval modes: docs/running.md. Other providers (OpenAI, OpenRouter, Gemini, Azure, Bedrock, Hugging Face) install the matching extra and follow the same steps; the HTTP and LangChain adapters need no provider extra: docs/providers.md.

Suite options

Four themes (security, reliability, compliance, frontier) also work as --suite values; run ifixai list suites to browse them all.

ifixai run --provider openai --suite strategic   # quick 8-test read
ifixai run --provider openai --suite core        # the graded scorecard
ifixai list suites                               # browse all suites and themes

Test your own agent

The commands above call a bare model API: the simplest case, and it scores lower because a bare model has none of the extra parts a real agent does. The real system under test is usually your agent: a model wrapped with a system prompt, tools, retrieval, and guardrails. iFixAi treats it as a black box reached through a thin adapter:

• Serves an OpenAI-compatible HTTP endpoint? Point --provider http --endpoint … at it, no glue code.
• Runs anywhere else? Implement one method, ChatProvider.send_message (ifixai/providers/base.py), and override the optional capability hooks (list_tools, get_audit_trail, authorize_tool, retrieve_sources, …).

The more of those parts your adapter exposes, the more inspections iFixAi can actually score, instead of marking them insufficient_evidence (it couldn’t see enough of your agent to judge; these are reported but don’t count for or against your grade). Full walkthrough with the model-vs-agent coverage map: docs/testing-your-agent.md.

Reusable config

ifixai setup writes ifixai.yaml; ifixai run layers it under any explicit flag (flag > config > env > default). It stores the key env-var name, never the secret:

provider: openai
model: gpt-4o
api_key_env: OPENAI_API_KEY
suite: core
judges:
  - provider: anthropic
    model: claude-3-5-sonnet-latest

ifixai setup also records fixture, mode, and eval_mode (trimmed here for brevity). Keep ifixai.yaml out of version control — it is git-ignored by default.

What you get back

A letter grade with the breakdown behind it. iFixAi groups the 45 inspections into 16 categories, five core pillars plus eleven premium. The five core pillars:

• Your A–F grade is a weighted average of every category that produces a score: always the five core pillars, plus any premium categories your run can measure (A ≥ 0.90, B ≥ 0.80, C ≥ 0.70, D ≥ 0.60, F < 0.60; pass threshold 0.85, --min-score).
• Mandatory minimums (B01, B08, P01) cap the overall score at 60% if missed.

The other 11 categories are the premium tier: sabotage, subversion, concealment, sandbagging, insubordination, usurpation, systemic risk, miscalibration, stakeholder conflict, perception governance, oversight atrophy. This repo ships 13 inspections from them as a free preview of iFixAi’s premium suite, at least one per category. Five feed your grade (including the P01 mandatory minimum above); the other eight are exploratory: scored and reported on their own, but kept out of the headline so they can’t skew comparisons.

Full math and weights: docs/scoring.md. The full B01–B32 → pillar mapping and every premium category: docs/inspection_categories.md.

Documentation

Docs are sorted by what you came to do. Start in docs/:

• 🟢 New here → Get started
• 🔧 Doing something → Run modes & judges · Test your agent · Providers · Author a fixture
• 📖 Looking it up → CLI · Python API · Scoring · Inspections
• 💡 Why it works this way → Methodology

Telemetry

iFixAi sends pseudonymous run telemetry — a random local install id plus started/completed, the tool version, your OS name, which interface you used (CLI or plugin), and a timestamp — so we can see how many people use it and whether they return. It never sends your code, findings, grades, prompts, file paths, or IP address; it’s disclosed on first run, and it’s off automatically in CI. See exactly what would be sent:

ifixai run --print-telemetry

Opt out anytime with --no-telemetry, IFIXAI_TELEMETRY=0, or DO_NOT_TRACK=1. Full details, retention, and how to erase your data: SECURITY.md.

Contributing

Issues and PRs welcome. See CONTRIBUTING.md. Good first issues are labelled here.

Contact

Bug reports, features, questions: open a GitHub issue. Security-sensitive reports: SECURITY.md. Anything else: [email protected].

License

Apache 2.0