Been working a lot on this on the side for a few months, it's stable enough now that I wanted to put it in front of people who actually run OpenTelemetry. So it reads the OTel traces your services already emit and flags I/O anti-patterns: N+1 SQL and HTTP, redundant calls, slow queries, excessive fanout, chatty services between services and pool saturation. It works at the protocol/span level, so it doesn't need to know whether you're on Hibernate, EF Core, SQLAlchemy or a bare driver, it just sees the queries they end up sending. Two ways to run it: as a one-shot CI gate on captured traces (exits non-zero past a threshold, emits SARIF so findings land in GitHub/GitLab code scanning), or as a long-running daemon that ingests OTLP and exposes Prometheus metrics, a query API and a self-contained HTML dashboard. Single static binary, no agent to attach to your runtime. It idles around 17 MB and in daemon mode it sustains roughly 1M events/sec at ~190 MB (peaks around 1.8M on the pipeline, benchmarked it on an M4 Pro in Docker). The piece I put the most care into is the energy/carbon scoring partly because while I'm a developer, I came with a personal background in environmental science and didn't want to just slap a green badge on it. Each finding gets an I/O intensity and waste ratio score, and it estimates CO2 per request with the SCI v1.0 / ISO 21031 formula. The default model is directional and I say so plainly: it ships a 2x uncertainty bracket and isn't a wattmeter replacement, so you can wire in measured energy ( Scaphandre RAPL , Kepler eBPF or Redfish ) and live grid intensity from Electricity Maps to tighten it. The methodology doc lists the actual sources instead of waving hands. To be clear about what it isn't: not a full APM, not a profiler, not (yet) an "official" regulatory carbon accounting tool (kinda already in a process with INR these days). If you want a full SaaS experience, Datadog or Sentry already do that. This is the narrow, lightweight bit the

submitted by /u/Fcking_Chuck [link] [留言]

Plug-in solar panels (no electrician needed) have just become legal in the UK and will go on sale soon. Helios estimates how much electricity a typical installation could generate at a given address and what that's worth against your tariff. It uses UK government LIDAR data to reflect the actual skyline, so it knows whether there's a building or a hill blocking the sun. Caveats: - Outside LIDAR coverage (most of Scotland and Wales) it falls back to a synthetic horizon (less accurate). - Trees an

A few weeks ago I posted about building a desktop app that automates GitHub commits because...

A new book looks into the long history of people who have opposed vaccines.

Keychron's K2 HE Concrete Edition sounds like a cute gimmick, but as I discovered, there's a really solid keyboard beyond the absurd choice of materials.

Kane Parsons was just 16 years old when he created a viral YouTube horror series based on a 4chan meme. Now he’s ready to conquer the big screen.

My paper was recently accepted to a workshop at CVPR-2026 as non-archival acceptance. Is it mandatory for me to register to the conference as I won't be able to attend(visa issues), but my friend will be there in the conference and can present on my behalf. I have few questions regarding my situation: Do I need to finish author registration for a non-archival workshop? Is it mandatory for me to have a poster in the conference venue? Will my paper get removed from the workshop website(where they list out the accepted papers) in case I don't register or not attend offline? Quick replies are appreciated as the deadline is pretty close. Thanks 🙏 submitted by /u/Sky6574 [link] [留言]

A practical, slightly opinionated Hermes Agent use case: an always-on chief of staff that remembers your work, schedules itself, uses tools, and reports back with receipts.

With inflation and gas prices rising, I’m trying to save money wherever I can. I tested Tello’s budget cell phone plan, and for me, it turns out prepaid can be just as good.

Finally got around making an portfolio site how is it ? (dev) submitted by /u/Miserable_Advice1986 [link] [留言]

Plus: A ransomware group is now stealing data in person, BusPatrol wants to hand its license plate surveillance data to the cops, and more.

The biggest risk in using open-ended LLMs for research is their tendency to hallucinate or invent sources. Andrej Karpathy's method of building an LLM Wiki addresses this by creating a closed-loop system: the model is trained only on your trusted raw source docs. This acts as a smart search engine for your own library, grounding all responses in verifiable documents. I've been using Recall, an AI knowledge base, to easily implement this closed retrieval system. It ensures that when Claude answers a question about my research, it's strictly based on the PDFs and papers I uploaded. Does anyone disagree that this closed-system approach is essential for high-stakes research? submitted by /u/AdarshXDD [link] [留言]

The company is hoping to sell 10 million wearables in the second half of 2026, according to 'The Information.'

I’ve added an Easter egg to my portfolio. I wanted to run "Bad Apple" I started experimenting with ASCII art, but then I remembered that you can also print pic in console in the latest versions of Chrome that bad apple didn’t exist before, so I went ahead and did it: NPM : https://www.npmjs.com/package/bad-apple-console Github : https://github.com/alienpingu/bad-apple-console full video : https://www.youtube.com/watch?v=lDpTDnPwZhk let me know if it is usable, thanks! submitted by /u/alienpingu [link] [留言]

I'm trying to understand the intuition behind Word2Vec training using a neural network. In Word2Vec (CBOW or Skip-gram), we often hear that the weight matrices learned during training contain the vector representations (embeddings) of words. However, I don't understand why the weights of the hidden-to-output layer (or output weight matrix) end up representing semantic features of words. Why do these weights become meaningful vector representations instead of just being parameters used to make predictions? I've explored multiple YouTube videos, blog posts and even asked ChatGPT several times, but I still haven't found an explanation that truly clicks for me. Most resources explain that the weights become embeddings, but not why this happens intuitively and mathematically. Could someone provide a clear intuition or mathematical explanation of why the output-layer weights end up encoding semantic information about words? Any good resources that explain this particularly well would also be appreciated. submitted by /u/aaryantiwari26 [link] [留言]