标签：#ha

I uploaded a 40-page PDF of an internal API spec, asked "what's the rate limit for the search endpoint?", and got back: "100 requests per minute per API key, with bursts up to 200. See section 4.2 of the document." With citations. In about three seconds. The whole stack runs on my laptop. It cost me $0 in LLM credits during development because Ollama is free and local, and the embedder I used is also free and local. The repo is here — issues and PRs welcome. This is the build log. Not a tutorial where every step works the first time — a build log where I tell you which decisions held up and which ones I redid. The problem most "chat with your PDF" demos have Every "chat with your PDF" tutorial I read in early 2025 had the same shape: open OpenAI, paste your API key, call gpt-4 with a 50-page PDF stuffed into the context window, get an answer, pay $0.03 per question, repeat. That works for a demo. It does not work for a tool you'd actually use at work, because: The PDF might contain customer data, internal pricing, or unreleased features. You do not want that going to OpenAI's training pipeline or anyone's logs. The cost adds up. If your team uses it 50 times a day, that's $45/month per seat. The model hallucinates on long PDFs anyway. Stuff 100 pages into a 128k context window and the model starts forgetting the middle. The fix is RAG (Retrieval-Augmented Generation) — don't send the whole PDF, send only the 3-5 chunks that are actually relevant to the question. The rest of the work is the same: embed the chunks, embed the question, find the closest matches, send those to the LLM with the question. But the cost and the privacy story both improve by 100x. The actual ask: Upload a PDF. Ask questions. Get answers from the document with citations, in under 5 seconds, with no data leaving my laptop and no monthly bill. The architecture One .NET 8 solution, one React app, one Ollama process, zero cloud dependencies. [ PDF Upload ] | v +-------------------+ chunks +-------

Last Tuesday my API gateway caught a NullReferenceException , streamed it to a dashboard in real-time, and pushed a draft code fix to the browser tab of the on-call engineer — before I finished reading the error myself. That sentence used to be vendor marketing. Now it's just my Program.cs . This is the architecture post-mortem. I built it on weekends. It runs in Docker. It cost me exactly $0 in LLM credits during development because Groq's free tier is generous and Ollama works as a swap-in. The repo is here — issues and PRs welcome. The problem most .NET teams have Production errors are caught, logged to a file, and forgotten. Engineers find out from a Slack ping twenty minutes later, if at all. By the time someone looks, the original request context is gone, the user's session has expired, and the stack trace is buried four layers deep in System.* calls. "Self-healing" is a word vendors use to mean "auto-restart the pod." I wanted something better. The actual ask: When an exception is thrown in service A, give the engineer (a) a clear root cause, (b) a suggested fix, and (c) a draft code patch — in under 30 seconds. Not a magic black box. Not an auto-applied patch. Just: catch the error, give the model the right context, push the analysis to a human in real-time, and let the human close the loop. The architecture One .NET solution, four projects, four NuGet packages, no new infrastructure beyond what you probably already have. [ HTTP request ] | v +-------------------+ enqueue +---------------------+ | SmartLogAnalyzer. | ---------------------> | Hangfire (Redis) | | Api | +----------+----------+ | (ErrorHandling | | | Middleware) | v +-------------------+ +---------------------+ | SmartLogAnalyzer. | | Worker | | (ErrorProcessingWorker) +-----+-------+-------+ | | AI call | | persist v v +-----------+ +-----------+ | Semantic | | MSSQL | | Kernel + | | (ErrorLog | | Groq LLM | | table) | +-----+-----+ +-----------+ | v +---------------------+ | SignalR Hub | | (

The service lets you share an entire folder - not just a single file - with anyone on the Internet in a single click. Under the hood, GET responses are hydrated with file fragments streamed over a duplex WebSocket connection. Close the browser tab, and the server disappears. Files can also be password-protected. I built it because I needed a way to share a static website with a client without first uploading the work to a cloud service.

https://archive.ph/tSlDZ https://www.cnbc.com/2026/06/22/alan-greenspan-former-chairm...

I got tired of MacOS failing to index applications (it consistently seems to prefer apps installed on my phone instead of the computer) so I've bound them to keys and have started building muscle memory for opening and switching apps. Reduces mental fatigue from constantly cmd + tabbing between apps all day

From fake tickets to cloned websites, AI is magnifying World Cup scams. Can fans distinguish between what’s real and what’s not?