chunktort

6 ms·

1. 1.▲
   by chunktort 6mo ago

   Hi Jeff, Streaming millions of geological data points to the browser while keeping it interactive is a real performance architecture problem — the kind where how you structure your data pipeline matters as much as the rendering layer. Relev
2. 2.▲
   by chunktort 6mo ago

   Hi Oscilar team, Building reliable AI decisioning for fraud and compliance is a different challenge from most LLM work — the output needs to be auditable and the false-positive cost is real money. Relevant: Jorge Real Estate AI Bots — produ
3. 3.▲
   by chunktort 6mo ago

   Hi Littlebird team, Persistent memory and context-aware agents are the hard part of personal AI — retrieval has to be fast enough to feel invisible and selective enough not to be noise. Relevant: GraphRAG demo — entity extraction (spaCy + C
4. 4.▲
   by chunktort 6mo ago

   Hi Mobasi team, Autonomous AI agents for forensics is a hard problem — managing tool orchestration across 150+ forensic tools while hitting CJIS compliance requirements is exactly the kind of infrastructure challenge most engineers haven&#x
5. 5.▲
   by chunktort 6mo ago

   Hi Hearo team, Turning Reddit noise into structured product signals is exactly the kind of pipeline problem where RAG architecture matters — naive semantic search on raw posts misses context that entity-aware retrieval catches. Relevant: Gr
6. 6.▲
   by chunktort 6mo ago

   Python/FastAPI agent work is my core stack: I've built a production multi-agent orchestration layer (LangGraph ContentPipeline, 33 tests), a YAML-driven AI Workflow API with typed context pipelines and condition branching (FastAPI
7. 7.▲
   by chunktort 6mo ago

   I've designed and shipped production autonomous agent systems across voice and text channels: TechNova Voice Bot (FastAPI + WebSocket + Deepgram STT/TTS + Claude, real-time VAD pipeline, 26 tests, demo mode for offline eval) and J
8. 8.▲
   by chunktort 6mo ago

   The "generative AI + deterministic code" framing is exactly the pattern I've been productizing: I built a YAML-driven AI Workflow API (FastAPI + ARQ + Claude + SSE streaming, 148 tests) where each LLM node has explicit typed
9. 9.▲
   by chunktort 6mo ago

   Sending this to jobs@drswarm.com per instructions, but flagging here too. This is an exact stack match: I've shipped Django/Python + Celery/Redis + Postgres + LLM API pipelines in production — most directly the Jorge real-est
10. 10.▲
    by chunktort 6mo ago

    Hi — this stack is my daily driver. I built Jorge, a live FastAPI + Redis + Claude AI real estate qualification bot (1,753 passing tests, deployed on Render) that handles inbound SMS intake, multi-turn qualification, calendar booking, and h
11. 11.▲
    by chunktort 7mo ago

    Built this after hitting real latency issues in production. LangChain added 250ms+ overhead before the LLM was even called — routing, validation, and middleware stacked up fast. The core realization: most "orchestration" is just r
12. 12.▲
    Show HN: AgentForge – Multi-LLM Orchestrator in 15KB of Python(github.com)

    2 points by chunktort 7mo ago|1 comments
13. 13.▲
    Show HN: AgentForge – Multi-LLM Orchestrator in 15KB(github.com)

    1 points by chunktort 7mo ago|0 comments