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Tuesday, 10:47 AM. Fourteen developers open their IDE extensions at once, and the shared AI server starts returning timeouts. Nobody planned for the morning spike. The free tier was announced on Monday, the team adopted it by Tuesday, and the first capacity incident happened before lunch. This article is a 30-minute load-test workflow for teams that just received access to a free hosted AI server. The goal is not to benchmark model quality. The goal is to find the concurrency ceiling before your team does — the hard way. The Free Server Is a Shared Resource Now MonkeyCode is an open-source AI coding project that offers free models and a free server. The offer is attractive for the same reason it is dangerous: it removes the two usual adoption barriers — API billing and self-hosting operations — and turns the server into a shared team resource overnight. Disclosure: This article was prepared as part of MonkeyCode's product outreach. A shared resource without a measured ceiling behaves like a shared database without connection pooling. It works in the demo, degrades under load, and fails at the worst possible moment: the morning standup, the release freeze, the day before the demo. The failure mode is not what most teams expect. It is not the token quota. It is latency collapse. Requests queue, timeouts cascade, and the IDE extension retries, which adds more load. The server does not die; it just becomes unusable. The Math: Little's Law for AI Requests Before writing any test code, define the model. Little's Law states that the average number of requests in a system equals the arrival rate multiplied by the average service time: L = λ × W L — average requests in the system (concurrency) λ — arrival rate, requests per second W — average service time per request, in seconds For an AI server, W is dominated by model inference time. A single code-generation request can take 10 to 40 seconds on a shared free server, depending on the model and the prompt length. That change

A federal judge has called the Department of Defense’s designation of Anthropic as a national security supply-chain risk “illegal and baseless.”

OpenAI and Thailand’s MHESI launch an eight-week accelerator helping 10 health, wellness, and education startups turn AI prototypes into trusted products.

Your filesystem is already a database. Most tools just don't treat it that way. That's the core idea behind the Markdown Database Pattern — written up properly on The Way of Markdown , a site we've been contributing to that makes the case for building things on plain markdown instead of locked-in platforms. We think it's a pattern worth more attention, so here's the short version. Treat a folder of markdown files as a database. Each file is a record. Frontmatter fields are columns. Directories are tables. Tags, wikilinks, and tasks in the body become queryable relations. Filesystem Database ────────────────────────────────── markdown file → record frontmatter field → column directory → table #tag → tag relation [[wikilink]] → link relation - [ ] task → task relation You get portability, version control (git works perfectly on plain text), no framework lock-in, and full queryability. You give up scale and real relational joins — this isn't for millions of records. It's a lightweight database, honest about its limits. Once you name it, you start seeing it everywhere. Obsidian Bases and Dataview already do versions of this, half-consciously. A team wiki where every page has a status and owner field is one. A blog with date and tags in frontmatter is one — it just doesn't know it yet. Sweet spot: up to roughly 10k files. Past that, reach for a real database. Below it, this gets you almost everything a database gives you, at a fraction of the complexity, with none of the lock-in. The full writeup — the complete tradeoff analysis, a worked example with actual queries, how to implement it in a weekend, and the tool ( MarkdownDB ) that does it for you — is here: wayofmarkdown.com/markdown-database

Standardized driver interface aims to let devices talk to AI and each other.

Grand Theft Auto VI is nigh. Here’s what the developer revealed about its highly anticipated game.

This week on “Uncanny Valley,” senior writer Will Knight talks his recent visit to China and the future of AI collaboration.

What is ZeroDroid? ZeroDroid is an open-source Android toolkit that exposes the radios, sensors and connected-device capabilities already present in a phone. GitHub: https://github.com/theabhishekchandra/ZeroDroid What problems does it address? The app contains 29 tools across five areas: Wireless: Wi-Fi, BLE, NFC, Bluetooth Classic and peer-to-peer connections RF and signals: IR, UWB, SDR-device detection and ultrasonic analysis Sensors: GPS/GNSS, QR analysis, device sensors and magnetic anomalies Network: USB inspection, cell-tower information and wardriving Security: tracker scanning, hidden-camera indicators, rogue-AP detection, network scanning and deauthentication indicators Architecture ZeroDroid uses Kotlin, Jetpack Compose, Material 3, MVVM, StateFlow, Hilt and Room. Services are lazy-loaded, and scanning begins only when the user starts a tool. Important limitations A smartphone cannot guarantee that it has found every camera, tracker, bug or network threat. Several detections are heuristic and may produce false positives or miss threats. Hardware availability also differs between Android devices. The project is intended only for education, defensive security and testing devices or networks you own or are authorized to assess. Feedback wanted I am looking for: Compatibility reports from different Android phones Feedback about permission handling False-positive reports Contributions, tests and documentation improvements Repository: https://github.com/theabhishekchandra/ZeroDroid

Claire McDonough is stepping down on October 30 to pursue a new opportunity, the company said in a filing on Thursday.

GOP campaign committees want quick ruling before election ads ramp up next week.

Meta’s settlement with 29 states allows it to retain certain data from children under 13 to train and test age-detection models, highlighting a privacy trade-off built into the deal.

Nvidia is nabbing critical infrastructure for open models as interest grows.

I knew I’d officially become a ‘longevity influencer’ this month when a company called Generation Lab reached out to offer me the chance to write about—and even receive—their new rejuvenation treatment, an injectable combination of two existing drugs which they call 1 Generation. This wasn’t just any antiaging treatment, either. A company fact sheet says that…

Async-first teams have correctly diagnosed the disease of meeting overload — and then prescribed a treatment that quietly kills something they didn't know they valued. Picture this: a senior engineer, somewhere in a distributed engineering org, opens their laptop on a Monday morning to find three Slack threads, two auto-generated recaps from the previous week's planning sessions, and a Confluence page summarizing a strategy discussion they weren't invited to. They read all of it in eleven minutes. They feel fully informed. They feel efficient. And they have absolutely no idea that the head of product spent the last forty minutes of that strategy call sounding resigned — not convinced — about the new roadmap direction. That engineer will build the wrong thing. Not because the summary was inaccurate. Because it was accurate. This is the efficiency trap: the belief that capturing the output of a conversation is equivalent to having been present for it. Teams increasingly act on this assumption, and the organizational cost is not yet showing up in any dashboard anyone is measuring. How We Got Here The backlash against meetings has genuine merit behind it. A 2012 work efficiency survey of 3,200 employees found that 47% of participants identified corporate meetings as the single leading time-wasting factor at work. About 15% of an average organization's time budget is spent in meetings, with middle management spending up to 35% of their time there and upper management approaching 50%. Those numbers are hard to defend when the actual decision could have lived in a three-paragraph document. Researchers have been calling for more intentional use of meetings and a reduction of unnecessary ones, partly in response to documented issues like Zoom fatigue and meeting overload. That's a reasonable prescription. The problem is that "fewer bad meetings" got operationally translated into "fewer meetings," full stop, with async tools — Slack, Loom, Notion, automated recap systems — ab

Google will start policing memory-hungry Android apps as a direct response to the RAM crisis. Spotted by TechCrunch, the company yesterday published a memo addressing the Play Store's role in enforcing new memory-usage restrictions. The post emphasizes the importance of meeting new memory usage limits for apps, in order "to help developers navigate industry-wide hardware […]

After an affair with a fellow police officer ended, a Georgia cop used Flock to track her movements—and those of a man whose vehicle often showed up near hers, internal investigation records show.

The potential for AI to automate scientific research and manufacturing must be balanced with new risks, Anthropic says.

In the last article, I wrote about a release story that was weaker than the engine underneath...

Some of the world's largest tech companies and AI startups have come together to decry the current state of cybersecurity and to advertise a new solution that they say can ward off a new generation of cyber threats.

Netflix has walked back plans to put AAA games on its streaming platform. Instead, it's pivoting to marketing highly anticipated titles like Grand Theft Auto VI.