[1] - https://www.npmjs.com/package/@anthropic-ai/claude-code/v/2....
Just point your agent at this codebase and ask it to find things and you'll find a whole treasure trove of info.
Edit: some other interesting unreleased/hidden features
- The Buddy System: Tamagotchi-style companion creature system with ASCII art sprites
- Undercover mode: Strips ALL Anthropic internal info from commits/PRs for employees on open source contributions
Also, not sure why anthropic doesn’t just make their cli open source - it’s not like it’s something special (Claude is, this cli thingy isn’t)
This is the single worst function in the codebase by every metric:
- 3,167 lines long (the file itself is 5,594 lines)
- 12 levels of nesting at its deepest
- ~486 branch points of cyclomatic complexity
- 12 parameters + an options object with 16 sub-properties
- Defines 21 inner functions and closures
- Handles: agent run loop, SIGINT, rate-limits, AWS auth, MCP lifecycle, plugin install/refresh, worktree bridging, team-lead polling (while(true) inside), control message dispatch (dozens of types), model switching, turn interruption
recovery, and more
I guess these words are to be avoided...
ANTI_DISTILLATION_CC
This is Anthropic's anti-distillation defence baked into Claude Code. When enabled, it injects anti_distillation: ['fake_tools'] into every API request, which causes the server to silently slip decoy tool definitions into the model's system prompt. The goal: if someone is scraping Claude Code's API traffic to train a competing model, the poisoned training data makes that distillation attempt less useful.I jest, but in a world where these models have been trained on gigatons of open source I don't even see the moral problem. IANAL, don't actually do this.
Could anyone in legal chime in on the legality of now 're-implementing' this type of system inside other products? Or even just having an AI look at the architecture and implement something else?
It would seem given the source code that AI could clone something like this incredibly fast, and not waste it's time using ts as well.
Any Legal GC type folks want to chime in on the legality of examining something like this? Or is it liked tainted goods you don't want to go near?
One neat one is the /buddy feature, an easter egg planned for release tomorrow for April fools. It's a little virtual pet, sort of like Tamagotchi, randomly generated with 18 species, rarities, stats, hats, custom eyes.
The random generation algorithm is all in the code though, deterministic based on you account's UUID in your claude config, so it can be predicted. I threw together a little website here to let you check what your going to get ahead of time: https://claudebuddychecker.netlify.app/
Got a legendary ghost myself.
https://daveschumaker.net/digging-into-the-claude-code-sourc... https://news.ycombinator.com/item?id=43173324
Original llama models leaked from meta. Instead of fighting it they decided to publish them officially. Real boost to the OS/OW models movement, they have been leading it for a while after that.
It would be interesting to see that same thing with CC, but I doubt it'll ever happen.
Not exactly this, but close.
There were/are a lot of discussions on how the harness can affect the output.
Copilot on OAI reveals everything meaningful about its functionality if you use a custom model config via the API. All you need to do is inspect the logs to see the prompts they're using. So far no one seems to care about this "loophole". Presumably, because the only thing that matters is for you to consume as many tokens per unit time as possible.
The source code of the slot machine is not relevant to the casino manager. He only cares that the customer is using it.
Or in short, if you give LLMs to the masses, they will produce code faster, but the quality overall will degrade. Microsoft, Amazon found out this quickly. Anthropic's QA process is better equipped to handle this, but cracks are still showing.
UNRELEASED PRODUCTS & MODES
1. KAIROS -- Persistent autonomous assistant mode driven by periodic <tick> prompts. More autonomous when terminal unfocused. Exclusive tools: SendUserFileTool, PushNotificationTool, SubscribePRTool. 7 sub-feature flags.
2. BUDDY -- Tamagotchi-style virtual companion pet. 18 species, 5 rarity tiers, Mulberry32 PRNG, shiny variants, stat system (DEBUGGING/PATIENCE/CHAOS/WISDOM/SNARK). April 1-7 2026 teaser window.
3. ULTRAPLAN -- Offloads planning to a remote 30-minute Opus 4.6 session. Smart keyword detection, 3-second polling, teleport sentinel for returning results locally.
4. Dream System -- Background memory consolidation (Orient -> Gather -> Consolidate -> Prune). Triple trigger gate: 24h + 5 sessions + advisory lock. Gated by tengu_onyx_plover.
INTERNAL-ONLY TOOLS & SYSTEMS
5. TungstenTool -- Ant-only tmux virtual terminal giving Claude direct keystroke/screen-capture control. Singleton, blocked from async agents.
6. Magic Docs -- Ant-only auto-documentation. Files starting with "# MAGIC DOC:" are tracked and updated by a Sonnet sub-agent after each conversation turn.
7. Undercover Mode -- Prevents Anthropic employees from leaking internal info (codenames, model versions) into public repo commits. No force-OFF; dead-code-eliminated from external builds.
ANTI-COMPETITIVE & SECURITY DEFENSES
8. Anti-Distillation -- Injects anti_distillation: ['fake_tools'] into every 1P API request to poison model training from scraped traffic. Gated by tengu_anti_distill_fake_tool_injection.
UNRELEASED MODELS & CODENAMES
9. opus-4-7, sonnet-4-8 -- Confirmed as planned future versions (referenced in undercover mode instructions).
10. "Capybara" / "capy v8" -- Internal codename for the model behind Opus 4.6. Hex-encoded in the BUDDY system to avoid build canary detection.
11. "Fennec" -- Predecessor model alias. Migration: fennec-latest -> opus, fennec-fast-latest -> opus[1m] + fast mode.
UNDOCUMENTED BETA API HEADERS
12. afk-mode-2026-01-31 -- Sticky-latched when auto mode activates 15. fast-mode-2026-02-01 -- Opus 4.6 fast output 16. task-budgets-2026-03-13 -- Per-task token budgets 17. redact-thinking-2026-02-12 -- Thinking block redaction 18. token-efficient-tools-2026-03-28 -- JSON tool format (~4.5% token saving) 19. advisor-tool-2026-03-01 -- Advisor tool 20. cli-internal-2026-02-09 -- Ant-only internal features
200+ SERVER-SIDE FEATURE GATES
21. tengu_penguins_off -- Kill switch for fast mode 22. tengu_scratch -- Coordinator mode / scratchpad 23. tengu_hive_evidence -- Verification agent 24. tengu_surreal_dali -- RemoteTriggerTool 25. tengu_birch_trellis -- Bash permissions classifier 26. tengu_amber_json_tools -- JSON tool format 27. tengu_iron_gate_closed -- Auto-mode fail-closed behavior 28. tengu_amber_flint -- Agent swarms killswitch 29. tengu_onyx_plover -- Dream system 30. tengu_anti_distill_fake_tool_injection -- Anti-distillation 31. tengu_session_memory -- Session memory 32. tengu_passport_quail -- Auto memory extraction 33. tengu_coral_fern -- Memory directory 34. tengu_turtle_carbon -- Adaptive thinking by default 35. tengu_marble_sandcastle -- Native binary required for fast mode
YOLO CLASSIFIER INTERNALS (previously only high-level known)
36. Two-stage system: Stage 1 at max_tokens=64 with "Err on the side of blocking"; Stage 2 at max_tokens=4096 with <thinking> 37. Three classifier modes: both (default), fast, thinking 38. Assistant text stripped from classifier input to prevent prompt injection 39. Denial limits: 3 consecutive or 20 total -> fallback to interactive prompting 40. Older classify_result tool schema variant still in codebase
COORDINATOR MODE & FORK SUBAGENT INTERNALS
41. Exact coordinator prompt: "Every message you send is to the user. Worker results are internal signals -- never thank or acknowledge them." 42. Anti-pattern enforcement: "Based on your findings, fix the auth bug" explicitly called out as wrong 43. Fork subagent cache sharing: Byte-identical API prefixes via placeholder "Fork started -- processing in background" tool results 44. <fork-boilerplate> tag prevents recursive forking 45. 10 non-negotiable rules for fork children including "commit before reporting"
DUAL MEMORY ARCHITECTURE
46. Session Memory -- Structured scratchpad for surviving compaction. 12K token cap, fixed sections, fires every 5K tokens + 3 tool calls. 47. Auto Memory -- Durable cross-session facts. Individual topic files with YAML frontmatter. 5-turn hard cap. Skips if main agent already wrote to memory. 48. Prompt cache scope "global" -- Cross-org caching for the static system prompt prefix
unreliability becomes inevitable!
Though I wonder how the performance differs from creating your own thing vs using their servers...
* Check if 1M context is disabled via environment variable.
* Used by C4E admins to disable 1M context for HIPAA compliance.
*/ export function is1mContextDisabled(): boolean {
return }
Interesting, how is that relevant to HIPAA compliance?
Like KAIROS which seems to be like an inbuilt ai assistant and Ultraplan which seems to enable remote planning workflows, where a separate environment explores a problem, generates a plan, and then pauses for user approval before execution.
I'd agree if it was launch-and-forget scenario.
But this code has to be maintained and expanded with new features. Things like lack of comments, dead code, meaningless variable names will result in more slop in future releases, more tokens to process this mess every time (like paying tech-debt results in better outcomes in emerging projects).
And now, with Claude on a Ralph loop, you can.
[1] https://www.tasking.com/documentation/smartcode/ctc/referenc...
> current: 2.1.88 · latest: 2.1.87
Which makes me think they pulled it - although it still shows up as 2.1.88 on npmjs for now (cached?).
Or is there an open source front-end and a closed backend?
It's a wake up call.
They could have written that in curl+bash that would not have changed much.
Claude code uses (and Anthropic owns) Bun, so my guess is they're doing a production build, expecting it not to output source maps, but it is.
Is that correct ? The weights of the LLMs are _not_ in this repo, right ?
It sure sucks for anthropic to get pawned like this, but it should not affect their bottom line much ?
I even made it into an open source runtime - https://agent-air.ai.
Maybe I'm just a backend engineer so Rust appeals to me. What am I missing?