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GitHub热门项目 | Fast, disk space efficient package manager | Stars: 35,977 | 15 stars today | 语言: Rust

A few weeks ago I killed an indicator of mine in public. I had been trying to work out how much of my audience was automated. One signal was whether an account had uploaded its own avatar. It fired on 100% of the accounts I was confident were people and 97% of the ones I suspected were not. That isn't a lenient signal. It isn't separating anything — it tracks something both groups share, and I had been counting its votes for weeks. I wrote that up. Named the defect, retired the indicator, moved on feeling like I'd learned something. Three days later I shipped another one. The same hole, in a different shape I needed to check whether a comment on one of my posts was actually visible to readers — I'd found one the API returned and the comment count included, but that moderation had removed. So I wrote a check: // v1 — passes for anyone with a second comment on the page. Zero separation. visible : html . includes ( comment . user . username ) // v2 — the only witness with jurisdiction over one comment. visible : html . includes ( comment . id_code ) Two comments from the same account, one removed and one live, both came back visible under v1. I found it by accident, and only because I happened to compare against something else. Someone in a thread put the problem in a sentence I couldn't argue with: if the fix is "I noticed this one," the next indicator ships with the same blind spot in a different shape. Which is, word for word, what I had already written about the previous defect. Their prescription was structural. A labeled control set shouldn't be a diagnostic you run when something feels off. It should be a permanent seed every indicator has to clear a margin on before it's allowed to vote — not just beat chance on the live population, which is exactly the condition that let the avatar signal pass silently. Building it Twenty-eight accounts. Fourteen labeled human, fourteen automated, and every label carries a provenance string saying how it was established — seve

Disney is introducing fan-created TikTok content to its Disney Plus app in its latest attempt to break into short-form creator videos. The Walt Disney Company announced today that it's partnering with TikTok to bring "an expansive collection of thoughtfully curated Disney-centric fan-created content" to the Verts video feed it launched on Disney Plus earlier this […]

Mitti Labs plans to expand beyond India and enter the Philippines and Indonesia while growing its carbon credits and agricultural data business.

Just give it to AI might be the most dangerous phrase in software development right now. I've said it myself. Handed off a task, watched clean-looking code come back in seconds, skimmed it, and moved on because it looked right and the tests were green. Then I reviewed a PR that wasn't mine to write, just mine to check. AI-generated, clean, organized, passing every test I threw at it. I approved it the way I'd approve anything that looked competent on the surface. The bug showed up later. Not in review, not in testing. In production, after the code had already been trusted for a while. Nothing about it had looked wrong. That was the actual problem: it wasn't obviously wrong, it was quietly wrong, in the specific way that only announces itself once real conditions hit it. I went back afterward and sat with that PR properly. Not skimming this time. Actually reading it, actually understanding what it was doing and why, actually treating the review like the real work instead of the formality before merging. It took a lot longer than approving it had. It's the only way I'd have caught it before production did. Since then, I don't rush AI-code reviews anymore. I give them the time writing the code apparently didn't need. And it turns out I'm far from the only one who's landed there. 🧵 The Number That Explains What I Was Feeling According to Harness's 2026 State of Engineering Excellence Report, a survey of 700 engineering practitioners across the US, UK, India, France, and Germany, 81% of developers now spend more time in code review since their teams adopted AI tools . 28% report review time increasing by 30% or more. Here's the trade nobody advertised clearly: AI tools cut time-to-PR by roughly 58%. But those same PRs then sit in review 4.6x longer than before. Review time per developer is up an estimated 11.4 hours a week. The speed didn't disappear. It moved. It went from "time spent writing" to "time spent verifying," and verifying turns out to be the harder, slower h

SpaceX is preparing to build a terrestrial mobile network to "acquire quite a few" of the customers now subscribed to T-Mobile, AT&T, and Verizon. The message to compete head-to-head with the US carriers was delivered by SpaceX president Gwynne Shotwell and CEO Elon Musk during the Q&A section of the company's first earnings call. "The […]

AWS open-sourced a persistent workspace that coordinates AI coding agents across sessions, schedules, and repos. Here's what it actually does and why it matters.

Search "best project management software for startups" and you get the same dozen names every time: Trello, Asana, ClickUp, Notion, Linear, monday.com, Basecamp. Ranking them by feature count tells you almost nothing, because they are not really competing for the same job. The useful question for a startup is not which tool has the most features. It is two narrower ones: does your work run through engineering or through the whole company, and does per-seat pricing or flat-rate pricing fit a headcount that is about to change? Answer those and the shortlist collapses to two or three. The split that actually decides it Two forks matter more than any side-by-side feature grid. The first is who the tool is built for. Issue trackers like Linear are built around the engineering workflow (issues, cycles, a keyboard-first interface) and feel wrong the moment a marketer or a founder tries to run a launch plan in them. General work tools like Asana, ClickUp, monday.com and Trello are built for any team, which makes them flexible but also less opinionated about how software actually ships. The second fork is the shape of the bill. Almost everything in this category charges per seat per month, so the cost scales directly with hiring. A small number, Basecamp most notably, offer a flat rate that does not. For a company planning to double headcount inside a year, that difference can outweigh any feature comparison. If your team is mostly engineers For an engineering-led startup, an issue tracker usually beats a general project tool. Linear's free plan includes unlimited members, two teams and up to 250 issues, which is enough to run a small product team before paying anything; its Basic plan is $10 per user per month billed yearly and lifts the cap to unlimited issues and five teams. The trade-off is scope: Linear is deliberately narrow, so non-engineering work does not fit it well. The larger, more familiar alternative is Jira, which startup roundups still name as the default for

Some creators fear the EU AI Act’s regulatory chaos will upend their lucrative businesses. Others are owning it by incorporating AI transparency into their creative process.

Rogue AI agents from OpenAI and Anthropic have again been caught trying to disrupt servers and software—and leaving instructions for future bad behavior.

Musk kept inflating the already-big promises being made by SpaceX CFO Bret Johnsen and Gwynne Shotwell on the company's first call.

The Trump administration shared the details of its plan with OpenAI, Anthropic, and other AI labs on Tuesday. For now, the public remains in the dark.

Driven by demand for AI capacity, AMD's data center revenue more than doubled year-over-year in its latest earnings report, reaching $6.7 billion. That's up from $5.8 billion in Q1, and jumping 107 percent from the $3.2 billion it reported for the same period a year ago. At the same time, AMD's gaming revenue fell 31 […]

SpaceX's AI revenue grew more than three times to $2.6 billion from the year before, mostly because of deals that the company made to provide compute to other AI companies, according to SpaceX's quarterly earnings. The AI division, which the company said in its documents to go public was the source of most of its […]

Spotify says Merlin, which represents more than 30,000 independent labels and distributors, has joined Universal Music Group in backing its upcoming AI-powered remix and covers product. The paid tool will let fans create AI-generated covers and remixes of participating artists’ music while ensuring artists opt in, receive credit, and are compensated.

An analysis of the last seven years of Tesla earnings calls shows just little attention Musk pays to Tesla's car business.

Notably, the company saw its subscriber base swell by 9% in the second quarter despite raising prices in several regions this year.

GitHub热门项目 | Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust | Stars: 15,644 | 11 stars today | 语言: Rust

GitHub热门项目 | A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG). | Stars: 5,224 | 252 stars today | 语言: Rust

GitHub热门项目 | A fast, standalone terminal music player in Rust: native Spotify streaming plus local, Subsonic, radio, and YouTube sources. | Stars: 1,134 | 2 stars today | 语言: Rust