标签：#AI

Disclosure: this is a write-up about my own side project — a combat analytics tool for AION 2. No affiliation with the game's publisher. Links at the end. Architecture A Windows desktop app: Rust backend + Tauri v2 webview UI . It passively captures the game's TCP traffic (npcap), reassembles streams, parses the game's undocumented binary protocol, feeds a combat model (damage, heals, buffs, deaths, boss detection), and pushes aggregates to a small JS frontend. Nothing touches the game client — no injection, no memory reading. If the packet didn't say it, we don't know it. Pain #1: the protocol is a moving target Nobody hands you a spec. The protocol is varint-heavy, partially compressed, and changes with game patches. You end up doing packet archaeology: capture a fight, stare at hex dumps, correlate "I pressed this skill at 19:32:04" with byte patterns, build a parser, and then — the fun part — keep it alive after every patch , usually reverse-engineering the diff within hours because your users' raids are tonight, not next week. One hard-won lesson: log everything behind toggleable trace categories. Our tracing setup keeps hot-path log callsites at literally zero cost when disabled (Rust tracing with Interest::never() + atomic per-category flags), so a user can flip a "Trace: Packets" checkbox, reproduce a bug, and send a log that actually contains the bytes we need. Pain #2: entity identity is a lie The single hardest correctness problem wasn't parsing — it was identity . A player is not one ID: your character has a stable "owner" entity that carries your buff bar, your damage lands under a transient combat entity whose ID changes between pulls, leave the dungeon and the game re-binds you to a brand-new ID, names arrive from different packets than damage, sometimes seconds later, sometimes never (mid-fight app start). Get any of this wrong and a healer's healing lands on a ghost row, or a player's buffs vanish from the saved fight because their ID was recycled 7

Roblox will be shutting down Roblox Connect, its video calling service introduced in 2023. Roblox Connect let you video chat with other people using your Roblox avatar, which would be able to mimic the movements you were making in real life. You could also run around with people on the call in a shared virtual […]

This weekend, cinephiles across the world will march to their local theaters to feast their eyes on Christopher Nolan's new adaptation of The Odyssey. It's on track to rake in anywhere between $80-$100 million in just a few days. People are clearly excited to see how Nolan uses cutting-edge filmmaking tech to make the Homeric […]

Neko Health has developed proprietary body-scanning technology, which it couples with bloodwork, to assess a person's health.

OpenAI, which is in the middle of a legal battle with Apple over hardware trade theft allegations, just released a light-up keyboard designed to be paired with its agentic coding app.

Submission for DEV's Summer Bug Smash — Smash Stories track. There was a file in my repo called run_benchmark_1_22.py . Not 1 to 24, which is what the harness was written to do. Not 1 to 26, which is how many Mersenne exponents the agents know. Twenty-two. A chart in the README — a2a_latency_times_1_22.png — agreed. At some point, past-me had decided the benchmark ends at 22, committed the evidence, and moved on. This summer, hunting for a Bug Smash target, I finally asked: why 22? The setup a2a-benchmark compares A2A agent performance across four languages. Python and Go sit behind Gemini tool-calling (ADK); Node and Rust are bare HTTP handlers. Each computes Mersenne primes with Lucas–Lehmer; a harness sweeps N from 1 to 24 and draws two charts. I ran the full sweep. At N=24, the Python column printed N/A . Every other language returned data. There it was — not a decision, a crash , worked around by shortening the run until it stopped hurting. The 4,300-digit wall The Python agent's response at N=24 wasn't even subtle about it: "Exceeds the limit (4300 digits) for integer string conversion; use sys.set_int_max_str_digits() to increase the limit" CPython 3.11 added a default cap on int→str conversion — 4,300 digits — as a denial-of-service mitigation. My agent stringified every prime it found. The 24th Mersenne prime, 2^19937−1, has 6,002 digits . Here's the part that made me laugh out loud: the stringified list was never returned . The tool reports only its elapsed time. The line that had silently amputated my benchmark at N=23 was decorative. The fix was git rm energy: delete the str() , keep the raw int. Go had the identical dead weight ( val.String() ) inside its timed region — it just happened not to crash. One deleted expression, and a column of data that had never existed came into being: N=24, Python, 2,425.9 ms. It gets worse before it gets better With the agents finally running, I kept pulling the thread. The harness parsed Python's elapsed time out of th

You have a project with 20 dependencies. Half of them are outdated. Running ncu -u or pip install --upgrade upgrades all of them at once — and when something breaks, you have no idea which package caused it. So you don't upgrade. The deps rot. Security patches pile up. loopgrade fixes this. It upgrades one dependency at a time, runs your test suite after each upgrade, commits if it passes, reverts if it fails, and moves on to the next one. When it's done, it gives you a full report of what was upgraded, what failed, and why. GitHub: https://github.com/Sagar-S-R/loopgrade PyPI: https://pypi.org/project/loopgrade/ Open for Contributors #Python #LangGraph #opensource

I gave one model the same 44-node architecture twice. The first time I asked for raw SVG — place every box, route every edge, hand me the coordinates. The second time I asked it to describe the same system as typed JSON and let a layout engine draw it. Same model, same session, same brief. The only thing I changed was the output boundary. The boxes are fine in both. I want to be upfront about that, because the usual version of this pitch is out of date. Models place labeled boxes well now. If you ask a current frontier model for a six-box flowchart as SVG you get a clean six-box flowchart, and if that's what you need, go do that — it's the right tool and I'm not going to pretend otherwise. What broke was the edges. With no routing algorithm the model just drew long diagonals straight through unrelated boxes. Not a few — everywhere the graph got dense. And when I changed one node, the entire hand-placed coordinate layout had to be regenerated, and came back different. That second part is the one that actually annoyed me. It's not a rendering bug you can squint past. The picture is a dead artifact: you can't diff it, you can't edit one box, you can't get the same one twice. Every change is a full regeneration and a fresh roll of the dice. This isn't a "wait for a better model" problem Here's the part I'd push back on if someone else wrote it, so let me make the case. Routing a connector around obstacles across a nested graph is global constraint optimization. It's the specific thing layout engines like ELK exist to solve. A model emitting SVG has to commit to an x/y for every point, in order, with no way to backtrack once it sees the whole picture — it's predicting the next token, not solving a layout. So a better model gives you nicer boxes, not untangled edges . The failure is structural, and I'd expect it to reproduce across models past a couple dozen nodes. If you don't buy that, the honest move is to test it: throw a 40-node architecture at whatever model you tru

pinto lets teams manage Scrum backlogs as plain text. Every backlog change can be inspected with git diff , carried across a branch, merged, and reviewed in a pull request—just like source code. The backlog follows the same lifecycle as the product it describes. pinto is local-first: no server, account, or hosted database is required. It works with AI agents, but never depends on them. And unlike a generic CLI to-do list, its core model is Scrum: Product Backlog Items (PBIs), Sprints, Kanban, and the metrics teams use to inspect and adapt. Why pinto exists Jira, Asana, and Notion are capable products. But as more features accumulate, the process can start serving the tool instead of the other way around: Creating one ticket means navigating required fields and workflow settings. Running a Sprint begins with configuring permissions, automations, or dashboards. Even a small status change feels expensive when the tool is slow to open. Scrum is meant to be a lightweight framework for rapid inspection and adaptation . When maintaining the board becomes work in its own right, the tooling has lost sight of that goal. The name pinto comes from the Japanese word ピント “focus.” That is also the design intent: keep the team's attention on the Product Backlog, the Sprint, and the work flowing across the board. pinto deliberately stays small. It starts quickly, keeps dependencies and vocabulary limited, stores durable data as readable files, and excludes project-suite features such as Gantt charts and CRM. Your backlog belongs in the repository Running pinto init creates a .pinto/ directory beside your code. Each PBI is a Markdown file with TOML frontmatter. Here is a real item from pinto's own backlog: +++ id = "P-2" title = "Investigate and fix Windows CI while retaining Windows support" status = "done" rank = "j" labels = ["ci", "windows"] created = "2026-07-15T06:24:58.731847Z" updated = "2026-07-15T09:34:50.653786Z" +++ # Summary Investigate, verify, and fix the current Windo

OpenAI's collaboration with keyboard maker Work Louder is available to order today.

Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains.

The stock has steadily fallen from the euphoric post-IPO high, showing that markets may be sobering up to the promises CEO Elon Musk made before and after SpaceX went public.

The stock has steadily fallen from the euphoric post-IPO high, showing that markets may be sobering up to the promises CEO Elon Musk made before and after SpaceX went public.

Inkling, a 975-billion-parameter open source model, was trained to understand video and audio. It could help Thinking Machines establish itself among competitors like Anthropic and OpenAI.

It's the company's first public proof point after a year and a half spent building AI infrastructure largely out of public view.

Someone on your team built an LLM feature. Maybe it's an inline code-suggest. Maybe it's a "fix this...

Suno data obtained in a hacking incident has exposed that the AI music generator was trained by scraping millions of songs and lyrics from online audio platforms, including YouTube Music, Deezer, and Genius, 404 Media reports. Given that Suno has avoided revealing what's in its training datasets and how they were acquired, this a rare […]

Google's biggest solar and battery project stands in sharp contrast with xAI's nearby unpermitted power plant.

The hacker used an employee's credentials to access source code, which revealed how Suno scraped decades of audio.

Livestream shopping platform Whatnot has acquired AI startup Shaped, a machine learning company focused on real-time recommendations and search. The deal will bolster Whatnot’s personalization and discovery features as it expands into new product categories.