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I’m researching patterns for edge / gateway telemetry where the network is unreliable (remote sites, industrial, fleets, etc.) and you need offline buffering + bounded disk + replay once connectivity returns.<p>Questions for folks running this in production:<p>What do you use today? (MQTT broker + ??, Kafka/Redpanda/NATS, Redis Streams, custom log files, embedded DB, etc.)<p>Where do you buffer during outages: append-only log, SQLite/RocksDB, queue-on-disk, something else?<p>How do you handle backpressure when disk is near full? (drop policy, compression, sampling, prioritization)<p>What’s your failure nightmare: corruption, replay storms, duplicates, “stuck” consumer offsets, disk-full, clock skew?<p>What guarantees do you actually need: zero-loss vs “best effort” (and where do you draw that line)?<p>What metrics/alerts matter most on gateways? (queue depth, replay rate, oldest event age, fsync latency, disk usage, etc.)<p>I’d love to learn what works, what breaks, and what you wish existing tools did better.
Hi HN, author here.<p>I built Tylax because I wanted to migrate my old LaTeX papers to Typst but found existing regex-based scripts too fragile for nested environments.<p>Tylax parses LaTeX into an AST (using mitex-parser) and converts it to Typst code. It supports:
- Full document structure (not just math snippets)
- Complex math (matrices, integrals)
- Experimental TikZ -> CeTZ graphics conversion
- Runs in browser via WASM<p>Repo: <a href="https://github.com/scipenai/tylax" rel="nofollow">https://github.com/scipenai/tylax</a>
Web Demo: <a href="https://convert.silkyai.cn/" rel="nofollow">https://convert.silkyai.cn/</a><p>Happy to answer any questions!
Hi HN, I’m the creator of YoloForge. I built this because I hit a wall with a hobby computer vision project: I needed a custom dataset, and zero-shot tools like Grounding DINO just weren't accurate enough for my specific classes. I decided I’d rather write code for a couple of weeks than draw another box by hand.<p>I previously experimented with Grounding DINO and SAM3. While they are amazing for generic objects, I found they struggle with specific semantic requests (e.g. specific manufacturing parts, game characters or distinguishing "a worker" from "a worker without a helmet").<p>I discovered that Gemini 3 Pro is surprisingly underrated for bounding box tasks if you prompt it with detailed visual descriptions. It handles semantic understanding significantly better than standard zero-shot detectors.<p>url: yoloforge.com<p>The Workflow:<p>Upload a zip of raw images (stored in Cloudflare R2).
Describe class/classes in plain English.
The system generates a .jsonl batch file and sends it to the Gemini Batch API. This allows us to process thousands of images in parallel at 50% of the standard cost.
You review/correct boxes in the UI and export the YOLO train/val/test dataset.<p>Technical Challenges:<p>One hard part was getting valid JSON out of the LLM consistently. I ended up writing a robust parser that uses regex fallback strategies to literally "salvage" valid bounding boxes from malformed responses.<p>The Stack:<p>- Frontend: Next.js
- Backend: FastAPI, Celery (for async zip processing and polling the batch API), Redis.
- Storage: Supabase (Auth/DB), Cloudflare R2 (Image Storage).
- Model: Google Gemini 3 Pro via Batch API.<p>There is a live demo on the landing page (no sign-up required) where you can upload a single image to test the detection logic. But of course the tool really shines with datasets that have thousands of images with multiple classes.<p>If you have any technical questions please ask!