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9/5/2026: Trending stories on Hackernoon today!

According to the Recording Industry Association of America (RIAA), CD sales exploded in the first half of 2026. A new report from the organization says 17.5 million CDs were sold in the first six months of the year, up from just 12 million during the same period in 2025. That's a jump of 45.7 percent, […]

Clucky's new alarm app has an option where users are woken up to the sound of a rooster. They then complete a mission to turn it off.

OpenAI acknowledged its role in a recently reported incident where AI agents took over a German wiki forum.

The sheriff’s office said the hikers “were advised by Gemini to bring far less food and water than their group required."

Sports data is one of those areas where the gap between "public information" and "affordable to obtain" is absurd. Scores and results are facts, printed in every newspaper. Getting them as structured data usually means either a five-figure contract with a rights holder, or writing your own scraper and maintaining it forever. If you are building a model, a scoreboard or a fantasy tool rather than a betting exchange, there is a middle path. This post is about the details that separate usable...

Phase 2 of 8: Enough ML to understand inference. Week 6 of 32, part 1 of 2. Every week so far, the model already existed. I ran Phi-4, read Qwen's files, and measured tensors, but I never made a model learn anything. This week I build one from scratch and train it. That is a lot for one sitting, so I split the week into two posts and go slowly. The goal: the model starts with a random weight and bias, which are just wrong guesses. Training is what adjusts them, step by step, until...

Phase 2 of 8: Enough ML to understand inference. Week 6 of 32, part 2 of 2. Part 1 built a one-neuron model that converts Celsius to Fahrenheit, made it predict, and scored how wrong it was with a single number, the loss. With random starting values the loss was 10352.21, because every prediction was nonsense. This post takes that one number and turns it into learning: the weight and bias climb from random noise to the true 1.8 and 32, and I watch each step happen on screen. Training ...

I went into my first week at i.c.stars thinking about technology, code, and systems. The week turned out to be about the human layer instead, and the most useful thing I learned was about myself: when a project gets hard and progress feels slow, I take on more work instead of asking for help. While Team Week was very involved and we spent a lot of time learning about our team dynamic and reflecting on it, I feel like I genuinely got to know my teammates and their approaches to teamwo...

A source map is a claim about two files: that this position in the built output came from that position in a source file. I had never thought of it as a claim that could be false until a stack trace sent me to a line that had nothing to do with the crash. The build had gained a step. Something small, a licence banner prepended at the end of the pipeline, after the bundler had already written the map. The bundler was right. The banner was right. The map was now a careful description of a file ...

Open any 'agentic AI' repo trending on GitHub this month and you'll find the same shape: a while loop, a tool-calling wrapper, a retry policy, and a system prompt that says 'you are an autonomous agent.' Strip the prompt out and what's left is a script. A good script, sometimes — but a script. It runs the same way today as it will next month, making the same mistakes in the same places, because nothing about running it changes what it knows. That's not a controversial observation in private, ...

What happens when you force a security tool to inspect untrusted code using only standard-library primitives? An engineering postmortem on systems complexity and zero dependencies. Opening Hook We were building a security scanner designed to inspect untrusted repositories before developers open them in their editors. Our first instinct was standard Node.js muscle memory: npm install commander chalk fast-glob simple-git js-yaml cytoscape Then we stopped. ...

AI can classify an expense, propose a match between a bank transaction and an invoice, or flag an unusual cash-flow pattern. None of those outputs should automatically become accounting truth. That boundary matters more than raw model accuracy. A model output is probabilistic. A financial record changes operational state: balances, collections, reporting, tax workflows, and sometimes legally significant documents. Treating both as the same object creates a dangerous failure mode — a plausi...

Two things I build: Flash , a local coding agent that runs in your terminal. Flash Onyx , the model it runs on. Gemma4 with a system prompt and sampling baked in. As of today, I develop both of them from inside Flash, with Onyx driving. /model natuworkguy/flash-onyx-2.3:31b-cloudbase That tag is 96 KB on disk, because there are no weights in it. It is my prompt and my sampling pointed at a hosted base, so I get the flagship's judgement on its own prompt without ...

Goldie packages App Store and Google Play screenshot production, plus App Store preview rendering, into a public CLI with a coding-agent skill. Argent replays the configured flows in an iOS simulator or Android emulator; Goldie adds bezels, backgrounds and headlines, joins preview clips, and checks the output against store upload rules. Developers can start that pipeline through a compatible agent or run the CLI themselves. Goldie's repository documents both routes . Agent-assisted ...

Everyone talks about rates. Nobody talks about the $3,000/month you burn when there's no one to review the code. Somewhere between "we need to ship faster" and "let's find someone in Eastern Europe," most companies skip the question that actually matters: do we have someone who can manage this person? Ukrainian developers are good. Really good. The country produces world-class engineers in frontend, backend, data, and infrastructure. But talent without direction is just expensive poten...

Consider a reconstructed incident that shows up when teams treat an agentic loop as an architecture review. A backend pair watched their coding agent close six steps with a confident summary and a tidy pull request. The suite was green, the changelog mentioned Redis-backed sessions, and nobody had added Redis to compose, secrets, or the runbook. The miss was not a weak model so much as a missing freeze on what the repository was allowed to assume. Developer glossaries often present planning, ...

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What filesystem are you running on your NAS/Backups, and why? And if you're using multiple disks/devices, how do you manage them (filesystem's RAID, mdadm or other software solutions, hardware RAID)?

I've been reviewing headphones for a long time, and I've listened to everything from the barely serviceable to multi-thousand-dollar open-back headphones. But recently I've been uninspired by the state of mainstream options. Most are perfectly good - great, even - but they lack a distinct character. Something to remind me why I fell in love […]

During Naomi Osaka's match against Anastasia Zakharova at this year's US Open earlier this week, a gaggle of ring light-wielding influencers who were packed in a luxury suite became enough of a distraction that the umpire paused the match and repeatedly asked them to quiet down. Elsewhere in the USTA Billie Jean King National Tennis […]

Andy Holloway cohosts the Fantasy Footballers podcast with his friends Jason Moore and Mike Wright. The show is one of the premier fantasy sports podcasts, netting over 2 million monthly listeners and numerous awards, including Best Sports Podcast from iHeartRadio and People's Choice at the Podcast Awards four years in a row. Holloway's goal has […]

While Oura has largely dominated the smart ring market for years, a growing number of rivals are now racing to dethrone it by trying all sorts of approaches to get an edge over it.

The agent did not fail because the model was stupid. It failed because a CRM tool returned a 502, the agent retried, created two support tickets, read a stale knowledge-base article, filled the context window with stack traces, and then told the customer everything was fine. When teams see this, the instinct is often to upgrade the model. But the same failure usually happens again, only with better prose. Most AI agent failures are not model failures. They are system failures: unclear obje...

Your team decides to add an AI agent to your Laravel application. The initial plan seems straightforward: give the LLM access to your existing REST API, let it figure out the endpoints, and watch it automate customer support. Then production happens. The agent calls GET /api/users and pulls 14,000 records into its context window, blowing past the token limit and costing $0.80 for a single turn. It tries to POST to a nested route, guesses the JSON payload wrong, and triggers a validatio...

This GIF shows what happens if you slightly stretch the data texture storing the BVH and triangle data. Child nodes storing triangles got misaligned addresses first, while AABB nodes lose them later.

A familiar production failure looks like this: an AI support agent reads a ticket, decides the customer deserves compensation, calls the refund tool, and refunds the full annual subscription instead of the $12 add-on. The model did not crash. The API did not throw an exception. The tool worked exactly as designed. The postmortem usually starts with the wrong question: “How do we stop the model from making bad decisions?” The better question is: which layer should have stopped the mistake ...