Ai

Everyone running DeepSeek-V4-Flash on DGX Spark hardware today is doing it with community forks: patched vLLM builds, custom images, source-built kernels. The received wisdom is that the stock NVIDIA NGC container cannot serve this model on GB10 (SM121) because upstream vLLM rejects every sparse-MLA attention backend on that architecture (vllm-project/vllm#45317, still open). We ran the experiment with only nvcr.io/nvidia/vllm:26.06-py3 on a two-node DGX Spark cluster (TP=2 over the 200G QSFP link). Policy constraint: no forks, no nightlies, no patched kernels. Here is what actually happens. ...

We moved an LLM backend from Claude Sonnet 4.6 to Opus 4.8. The model is configurable through a single environment variable, so this should have been a one-line change. Instead, every call started returning HTTP 400. The error, once we read the full response body: anthropic.BadRequestError: Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': '`temperature` is deprecated for this model.'}} Opus 4.8 no longer accepts the temperature parameter. Send it, even temperature=0.2, and the API rejects the whole request. Our code passed temperature on every call (a habit, for slightly more deterministic synthesis), so the model swap broke everything until we stopped sending it. ...

We expose our internal knowledge base over both REST and MCP using fastapi-mcp, which mounts existing FastAPI routes as MCP tools: no second server, no protocol-translation proxy. Two tools matter here: search_kb (semantic retrieval) and ask_kb (retrieval plus an LLM synthesis call that returns a cited answer). search_kb worked flawlessly everywhere. ask_kb failed, intermittently, and the failure was maddeningly opaque. In the MCP client it surfaced as nothing more than: Command failed with no output No stack trace. No error payload. Just silence. And only sometimes. ...

In a previous post we rebuilt our company knowledge base server from an MCP SSE endpoint to a plain FastAPI REST server because MCP client support was too fragmented to be reliable. The conclusion was: REST is the pragmatic choice, MCP can come back when the ecosystem matures. The ecosystem has matured faster than expected. We added MCP back on top of the FastAPI server and it now works across every client simultaneously: claude.ai, Claude Teams (company-wide), Claude desktop, ChatGPT, and OpenWebUI. This is what we learned in the process. ...

We built an internal knowledge base server to give our AI agents access to Conselara’s company data: capabilities, past performance, GSA rates, certifications. The idea was straightforward: expose it as an MCP server so any AI client could query it semantically. It worked in Claude Code. It worked nowhere else. What MCP promises The Model Context Protocol is Anthropic’s open standard for connecting AI models to external tools and data sources. The pitch is compelling: define your server once, and any MCP-compatible client can call it. Claude Code has native MCP support. The ecosystem is growing. ...

Federal health research platforms have a specific challenge with AI: the data is sensitive, the accuracy bar is high, and the compliance requirements are real. You cannot send protected health information to a commercial API, and you cannot publish AI-generated content to a national audience without review. But the volume of work, spanning literature, publications, content, and code, is large enough that ignoring AI entirely leaves real efficiency on the table. ...