autonomous developer tools
three tools.
zero compromise.
One runs your code autonomously. One maps everything you've built. One automates your developer marketing.
all three live on your machine · your key, your data
Describe the task. It ships it — reads code, edits files, runs tests, commits.
Understand any codebase in 30 seconds. No LLM, no guesses — pure AST analysis.
Write once, post everywhere. AI-generated content for Reddit, dev.to, and Twitter. Approve before publishing. Track every cent.
↓ scroll for full details
the principle
maximum deterministic value.
in every condition.
The core of every tool is deterministic — same input, same answer, no model required. An AI brain only makes it faster; it never decides the truth. So the value never depends on a key, a cloud, or a vendor staying online.
0 LLM tokens
Code analysis, graphs and risk are pure computation. Nothing to pay per query.
0 hallucinations
Reproducible output, identical five years from now. A model can't lie about what is actually there.
your data stays
Everything runs on your machine. Your key, your code — nothing leaves.
immortal — works with any brain, or none
cloud brain
Drop in a DeepSeek key → full autonomous agent loop at cloud speed.
local brain
No cloud key? Point it at a local model (Ollama). Same agent, fully offline.
no brain at all
The MCP server + 51 tools still stand. Whatever LLM you already use discovers them and drives them itself.
Three tiers, one guarantee: the tools never stop producing value. The brain is swappable — the value is not.
deepstrain
autonomous execution runtime
A terminal-native AI agent that takes a task and runs it to completion. Not a suggestion engine — an executor. It reads your code, edits files, runs your tests, and keeps looping until everything passes.
not a copilot. an autopilot.
It doesn't suggest what you should type next. It runs the task end-to-end — reading files, making changes, verifying results — without you watching.
reasoning, not completion.
DeepSeek R1 thinks through problems before acting. Give it a complex refactor or a failing test suite — it plans, then executes.
18× cheaper than GPT-4.
BYOK architecture. Requests go direct to platform.deepseek.com. ~$0.27/M tokens. A typical bug fix costs less than a coffee.
pip install deepstraindeepstrain chatstrain chat "ship this feature"
// what you get
Reads your codebase, edits files, runs tests, fixes errors — on its own. You describe the task, it does the work.
Chain-of-thought model, not just autocomplete. Architecture decisions, multi-file refactors, complex debugging.
Filesystem · git · shell · web · reasoning · memory. Everything it needs to complete a task end-to-end.
Your DeepSeek API key, your hardware. Requests go direct to DeepSeek. We never see your code.
Nuitka-compiled .pyd/.so, ~4 MB, distributed via PyPI. No source exposure, no interpreter overhead.
HMAC-signed license, works without internet after first activation. Edge revocation built in.
// pricing
professional
cancel anytime
ATLAS
deterministic code intelligence
Know your codebase before you touch it. Pure AST analysis — no LLM, no guesses. Dependency graphs, security scans, hotspot detection. One HTML report that works offline, forever.
no hallucinations.
Pure AST analysis. Same codebase always gives the same output. No probabilistic guesses, no model errors — just facts.
works offline.
Scan your codebase on a plane. Share the HTML report without spinning up a server. No account needed after activation.
30 seconds to full picture.
221 files, 1563 symbols, dependency graph, security scan, code health metrics — one command, half a minute.
// analysis modules
11 totalcoreLines, complexity, function count. Baseline metrics across every file.
system_mapFull import dependency graph. Know which modules are load-bearing before you touch them.
risk_radarRanks files by change risk — coupling + churn + complexity. Know where not to poke.
security_shieldScans for insecure patterns: hardcoded secrets, SQL injection vectors, unsafe deserialization.
code_healthDuplication, dead code, test coverage gaps. Measures tech debt objectively.
atlas_mcpMCP server: expose atlas data to any AI coding assistant in real time.
decision_centerAI-assisted refactor planner. What to extract, consolidate, or retire — ranked by impact.
ownership_mapMaps files to git authors. Who owns what, who needs to review what.
rewindHistorical snapshots of codebase health. Track debt and quality over time.
what_ifPre-merge impact analysis. Simulate what breaks if a file changes.
commit_guardPre-commit hook integration. Block commits that introduce new security issues.
// pricing
ADAUTO
narrative cognition infrastructure
Developer marketing that remembers. Runs on your machine, learns from engagement, posts only after your approval. Strategy built-in — LLM just writes the words.
approval gate — always on.
Every post lands in pending_approval first. Nothing publishes without your review. Editing and rejecting are first-class.
engage → learn → adapt.
Tracks upvotes, comments, and replies per post-type. Exploits what works, explores what hasn't been tried. ~$0.00034/post.
5 tools. minimum tokens.
run · status · approve · post · report. GET / returns ~208 tokens. Any LLM — Claude, GPT, local — can drive a full campaign loop.
pip install adautoadauto serveadauto run --campaign launch-week// pricing
deepstrain+ATLAS+ADAUTO
Atlas maps what you built. deepstrain improves it. Adauto tells the world about it. Three tools, one cognitive loop — understand, execute, narrate.
// how they fit together
use deepstrain when
- →you want the work done, not suggested
- →shipping a feature or fixing a bug
- →running tests end-to-end autonomously
- →you need a BYOK reasoning agent
use atlas when
- →you're onboarding onto a large codebase
- →running a security audit before deploy
- →deciding what to refactor next
- →tracking tech debt over time
use adauto when
- →you ship and need the world to know
- →content decays faster than you can write
- →Reddit/dev.to posts sit on your todo list
- →you need ROI tracking per post
use all three when
- →atlas maps → deepstrain ships → adauto narrates
- →full visibility + full execution + full presence
- →you run a serious production product
- →complexity scales, narrative must keep up
when the cloud stops, the work does not
Claude hit its limit.
deepstrain didn't.
Every cloud AI has a session cap. When yours resets in 5 hours, you have two choices: wait — or switch the brain without losing the tools.
01
Claude hits the limit
Session ends. Context is gone. The tools are not — deepstrain's 51 tools are local, always on.
02
Switch the brain
One command. deepstrain points at your local Ollama model (qwen2.5-coder, llama3, any GGUF). Same tools. Same data. No wait.
03
Continue from here
handoff.py exports the session summary. The local LLM reads it. You pick up exactly where you left off.
// switch brain in one step
set DEEPSTRAIN_BASE_URL=http://localhost:11434/v1 deepstrain chat # same 51 tools, local brain # or use the handoff helper: python handoff.py # exports session → injects context → opens chat
works with Ollama · LM Studio · any OpenAI-compatible local endpoint
Those 5 hours create a hunt signal.
Every day, thousands of developers post "Claude hit its limit, any alternatives?" on Reddit, HN, and dev.to. adauto watches those threads. When one appears, it drafts a genuine reply — deepstrain as the local-first alternative that keeps working. Human-approved before it posts. Zero spam.
hunt → respond → approve → post · adauto closes the loop