https://nitter.cf/t.co/WIInSHSiO1 Founder
Joined October 2008
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Alex Patterson is working on @CodingCatDev retweeted
Stuffing an entire docs repo into a system prompt kills latency and explodes token costs. Context MCP flips the architecture: agents load a lightweight index via initial_context, then fetch exact nodes on demand. Full breakdown in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Token waste is also a precision problem. When AI agents ingest bloated retrieval payloads, hallucination rates climb alongside inference bills. Governance starts with scoped GROQ projections and structured dataset outlines. Production notes below 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Cloud agent pipelines chain network hops across transcription, inference, and tool routing. Moving Speech-to-Text and a 12B Gemma model directly onto the device eliminates round-trip latency and keeps PII inside local memory. Architecture in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
How does Context MCP give AI agents deterministic context without blowing up your token budget? Instead of stuffing 50 RAG chunks into the prompt, the agent inspects the tree outline and pulls only the exact Markdown paths it needs. Details below 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Vector RAG often fails when AI agents need exact technical answers. Chunking splits related rules apart and burns tokens on noisy retrieval. Context MCP fixes this by exposing a structured knowledge base directly to your agent. Breakdown in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Production AI governance isn't just prompt guardrails. If unapproved doc edits can silently overwrite your retrieval layer, your agent isn't safe for enterprise workflows. Keep your knowledge base verified and auditable. Guide in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Vector similarity search often returns half-broken chunks when AI agents need exact rules. With Sanity Knowledge Bases and Context MCP, agents read the structured outline first and fetch complete Markdown entries by path. Breakdown in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Context MCP: Give AI Agents Precise Knowledge Base Access. Connect AI agents to a knowledge base with Context MCP. Load the outline, choose exact entry paths, and read full Markdown entries, without relying on similarity search to find the right chunk.
Alex Patterson is working on @CodingCatDev retweeted
Instead of letting an agent guess which vector chunk matters, start with two things in the config: a sharp dataset purpose and a focused GROQ projection. That produces a clean outline for direct entry-path reads via Context MCP. See it in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Sending sensitive patient data to a cloud LLM API just to parse a voice request is a massive HIPAA compliance headache. With local Speech-to-Text and a 12B Gemma model, you can execute tool calls 100% offline on-device. Breakdown in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Offline On-Device AI Agents with Gemma & Local Tool Calls (feat. @howdevelop)
How do you run an agentic healthcare workflow without sending private patient data to the cloud? Run local Speech-to-Text + a 12B Gemma model on-device to execute pharmacy tool calls 100% offline.
Alex Patterson is working on @CodingCatDev retweeted
Running AI agents in production comes down to unit economics and governance. Combining a lightweight `initial_context` outline with scheduled build diffs keeps token costs low and catches source drift early. Production notes in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Shipping an MCP knowledge pipeline to production requires three guarantees: low token overhead, source drift protection, and strict read-only auth. Never let unvetted doc edits silently mutate agent answers. Production patterns in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
What happens when someone edits a pricing or policy doc with an unverified claim? In standard RAG, the vector index ingests it immediately. With Sanity Knowledge Bases, the conflict is flagged before it touches agent state. Demo in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
How do you keep AI agent context windows lean at runtime? Shape the dataset at build time. Use a narrow GROQ projection to strip UI metadata and compile a compact dataset outline so agents retrieve exact entry paths on demand. Details in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Anthropic has filed an S-1 registration statement with the SEC related to its proposed IPO.
Read more: anthropic.com/news/s1-sec
Legal disclaimer: A registration statement relating to these securities has been filed with the SEC but has not yet become effective. These securities may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective. This post does not constitute an offer to sell or the solicitation of an offer to buy these securities, nor shall there be any sale of these securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction.
Alex Patterson is working on @CodingCatDev retweeted
Stop Wasting Tokens: Give AI Agents a Dataset Outline. Keep AI agents accurate and token-efficient by replacing broad similarity searches with a lightweight dataset outline and direct entry-path retrieval. Start with a clear purpose and a narrow Groq proj
Alex Patterson is working on @CodingCatDev retweeted
Broad vector similarity searches burn tokens on noisy chunks and miss structured context. Instead of dumping raw RAG hits into the prompt, give AI agents a lightweight dataset outline with direct entry-path retrieval. Full breakdown in the reply 馃憞
Alex Patterson is working on @CodingCatDev retweeted
Documentation never stays static. Instead of rebuilding your entire knowledge index blindly, Sanity Knowledge Base runs scheduled weekly or monthly refreshes, diffs sources against your last build, and flags material changes. See it in the reply 馃憞