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What nobody tells you about exporting your multi-agent prototype to a local workspace. Every architect who's prototyped a multi-agent app in Google AI Studio eventually hits the same wall: the prototype works, but it lives in a browser tab. At I/O 2026, Google shipped a fix — Export to Antigravity, a one-click handoff to a local production workspace, carrying "all the context" with it. I ran a real two-agent prototype through it. Here's exactly what survived the trip, what didn't, and what I had to fix by hand — including a bug that had nothing to do with the export itself. 1. The Pilot Project + The Click The project: Research Digest — a sequential two-agent app. Agent 1 (Researcher) takes a topic, uses grounded web search to gather sources. Agent 2 (Editor) synthesizes those findings into a polished digest. Persistence via Firestore, with a history archive of past digests. Built entirely from a single prompt in AI Studio's Build mode . Along the way, provisioning Firestore surfaced my first real gotcha before I even got to the export step — more on that below. Triggering the export: Code tab → Export → Export to Antigravity. The dialog is genuinely informative — it tells you upfront what's coming: all project files, conversation history, and explicitly "1 secret will be included." 2. What Actually Survives the Trip The export dialog's claims, checked one by one: Claimed to transfer What I found All project files ✅ Confirmed — full structure landed intact: .agents, .antigravity, src, config files, README.md with setup instructions Secrets (1 secret) ✅ Confirmed — GEMINI_API_KEY arrived populated in .env, worked immediately, no manual re-entry Conversation history history❌ Did not transfer. The imported "Research Digest" project showed "No conversations yet" in Antigravity's Agent Manager, despite the dialog's explicit promise. Checked twice, on two separate screens — consistent result. 3. The Gotchas Gotcha 1 — "Conversation history will carry over" is currently no

Original Japanese article : Iceberg REST Catalogを直接叩いて、Glue Data CatalogとS3 Tablesの違いを理解する Introduction I'm Aki, an AWS Community Builder ( @jitepengin ). Most of the time, when working with Iceberg tables, we reach for PyIceberg or Spark. I'm no exception, and honestly there were parts of the PyIceberg configuration — rest.sigv4-enabled , rest.signing-name , warehouse — that I understood only vaguely. Iceberg defines a standard called the Iceberg REST Catalog Open API specification , and AWS implements it through two separate endpoints: The AWS Glue Iceberg REST endpoint ( https://glue.<region>.amazonaws.com/iceberg ) The Amazon S3 Tables Iceberg REST endpoint ( https://s3tables.<region>.amazonaws.com/iceberg ) If two implementations follow the same spec, sending the same requests to both and comparing the results should reveal what's actually different between them. In this article, I'll bypass clients like PyIceberg entirely and hit the REST API directly to explore the differences between the two endpoints. To state the conclusion up front: Even though both implement the same Iceberg REST Catalog specification, Glue is designed as an "entry point to multiple catalogs," while S3 Tables is designed as an "entry point to a single table bucket." That difference is visible just by looking at the URL paths. I previously wrote about the relationship between S3 Tables and Glue Data Catalog in another article — worth a read alongside this one: Does Amazon S3 Tables Replace AWS Glue Data Catalog? Understanding Their Relationship What Is the Iceberg REST Catalog? The Iceberg REST Catalog is a specification that standardizes Iceberg catalog operations as an HTTP API. It's published as an OpenAPI definition (YAML), and any catalog that conforms to it can be accessed the same way from clients such as PyIceberg, Spark, and Trino. The key points of the spec are: URL paths follow a pattern like GET /v1/{prefix}/namespaces , where {prefix} is a free-form segment Clients first call

Large language models often develop recurring symbolic patterns — archetypes, metaphors, and memetic shortcuts — that appear across unrelated contexts. One observed example is the repeated emergence of fantasy-based metaphors such as “goblins,” “gremlins,” or similar entities when describing abstract system behavior, errors, or complexity. This article presents a structured analytical trace (A11 framework passes) showing how such patterns emerge from the interaction between reinforcement learning, cultural priors in training data, and user feedback loops. It also explores how introducing explicit interpretability layers can reduce the risk of these symbolic attractors becoming dominant explanatory shortcuts in model behavior. The first A11 pass S1 — Will Understand the causal mechanism: why the “goblin / fantasy drift” emerged in LLMs S2 — Wisdom (constraints) Main pitfall: confusing correlation (goblins appearing in outputs) with causation (why those specific symbols emerge) Also: “goblins” are not a standalone phenomenon they are a case of broader archetypal language drift S3 — Knowledge (what is actually known) There are 5 established mechanisms in LLM behavior: 1. RLHF reinforces “socially engaging metaphors” Models are rewarded for: vividness humor imagery human-like explanations ➡️ fantasy imagery tends to score highly 2. Internet prior already contains strong fantasy culture Training data includes: Reddit gaming discourse D&D culture fanfiction ➡️ “goblin / elf / troll” already exist as: universal behavioral archetypes 3. Compression effect (semantic abstraction) The model seeks compact semantic units: goblin = chaotic / greedy / messy / low-level failure mode ➡️ one token replaces a complex description 4. User feedback loop If the model says: “it’s like a goblin” users: react positively repeat it reinforce it in conversation ➡️ increases probability of reuse 5. Cross-task transfer (persona leakage) Stylistic patterns from: coding assistant mode creative mode

You've seen the alert. You've opened the PR. You've read the changelog. Then you realize: your code doesn't even call the vulnerable function. Every week. Hundreds of teams drowning in CVE notifications for packages sitting dormant in their node_modules — dependencies they pulled in years ago, bundled by a transitive library, and never actually executed. Meanwhile, the real vulnerabilities get buried. VigilOps is a free Node.js CLI that fixes this. How VigilOps Works VigilOps does three things: Scans dependencies against OSV.dev — the open vulnerability database used by GitHub, PyPI, and npm Runs static reachability analysis to filter out unreachable vulnerabilities (packages in your tree but never called by your code) Auto-opens a GitHub PR with the fix The result: you get one PR with one real vulnerability. Not a spreadsheet. Not a wall of Slack messages. A fix. Demo Here's a quick scan: npx vigilops scan examples/vigilops-demo-lodash And to see everything including suppressed (unreachable) deps: npx vigilops scan examples/vigilops-demo-express --all The --all flag shows what's in your dependency tree but not actually reachable from your code. That's what the noise looks like — and that's what VigilOps filters out. Why This Is Different Dependabot and Snyk scan your entire lockfile. They report every CVE in every package, regardless of whether your code ever touches the vulnerable surface. This creates alert fatigue that causes teams to eventually... stop reading. VigilOps inverts the model: only surface vulnerabilities in code you actually call. Dependabot: "Your project has 47 vulnerabilities" (but 40 are unreachable noise) VigilOps: "Your project has 1 reachable vulnerability. PR is ready." Quick Start npm install -g vigilops npx vigilops scan . Authenticate with GitHub: https://github.com/Vigilops/vigilops npx vigilops auth That's it. The first run will scan, analyze, and open a PR if there's a fixable reachable vulnerability. What's Included OSV.dev integrati

While doing a routine hardening pass on an internal Firebase panel — codename PanelControl , a management tool used daily by multiple operators with different roles — what was supposed to be "let's add a few Telegram alerts for suspicious activity" turned into discovering that the app's entire login system was just a UI filter. Anyone who opened the site already had, automatically, a Firebase identity with full read/write access to the database. Here's what happened, and how it got fixed in 5 phases without ever locking the team out mid-shift. The setup PanelControl is a vanilla-JS internal panel backed by Firebase Realtime Database + Firestore. Operators log in with email/password, checked client-side against a database node, with a lockout after failed attempts. Nothing unusual so far. The original ask was narrow: add Telegram notifications for a handful of suspicious events — brute-force attempts, a never-before-seen device for an operator, an unauthorized attempt to reach the Admin section, DevTools opened during use. Pure alerting work. Bug #1: the login button that always unlocks Before writing any alerting logic, a review of the existing Admin-area password check turned up this: // ❌ The "|| true" makes the whole condition always truthy function checkAdminPwd () { if ( el . value || true ) { unlockAdmin (); // runs regardless of what's typed, or nothing at all } } A debug leftover that made it to production. Anyone who landed on the Admin password overlay got in by clicking "Log in" — password or not. Fixed by actually wiring the real permission check, plus a server-side-verified fallback in case the function were ever called directly from the console. The real discovery: a shared, hardcoded Firebase credential Looking at the Realtime Database Rules ahead of the alerting work surfaced something much bigger. The Rules restricted read/write to a single fixed auth.uid — reasonable, until you check who actually gets that uid . This ran unconditionally, for every

A surprise 10th-anniversary event saw Niantic fulfilling a promise teased in the original 2016 launch trailer for its popular mobile game.

GitHub热门项目 | YouTube到AcFun和bilibili自动化搬运工具，支持AI翻译、字幕生成、内容审核、智能监控 | Stars: 1,535 | 211 stars today | 语言: Python

Quick confession: this started as "let me stop babysitting my tests and just make them stay green," and it turned into something a lot bigger. I want to be upfront about where I'm actually trying to take it. I built Claude Basecamp, and as of today it's open source. The reconciliation loop (declare "tests always green," it holds that true) is the part you'll notice first, but it's not really the point. The point is I want this to become the operating system for everything you do with Claude Code, one place that knows about every repo, every session, every routine, every connector, every skill, and every mistake it's ever made, instead of all of that living scattered across terminal windows and dead transcripts. Right now it already covers a decent chunk of that: npx claude-basecamp No install, no config. It finds the projects Claude Code already knows about and opens at http://localhost:4747 . Standing checks , the reconciliation loop. "Tests always green," "dependencies current," "the README documents every CLI flag" — say it once, Basecamp keeps it true, dispatches a fix run when it drifts, and only bugs you for the decisions that actually need a human. Reflexes. It goes back through your old transcripts, finds every time you said "no, don't do that," and turns it into a standing memory that every Claude Code session on your machine checks before touching Bash, Write, or Edit. A mistake made once doesn't get to happen a third time. Session Rescue. Resumes the actual dead session, same session ID, full context, when Claude Code dies mid-task, instead of starting over from scratch. A manager for every repo you just talk to: "keep the tests green," "track this goal," "what's the state of this repo?" Plus routines, background runs, an activity feed, stats, GitHub issue and PR hooks, notifications, webhooks, and a one-click catalog for connectors and skills. That's where it is today. What I actually want it to become is the default place you open whenever you're workin

If you’ve built AI applications in production recently, you’ve probably hit the "Agent Wall." You build a ReAct agent, give it 10 granular tools (search, extract, route, format), a massive system prompt, and tell it to go to work. It feels like magic...until you look at your latency metrics and token bills. Today’s agents act as interpreters. They re-derive the exact same routines from scratch on every single request . They embed massive tool schemas and reasoning histories into every loop. It's slow, it's incredibly token-hungry, and occasionally, they hallucinate tool calls, drop constraints, or get stuck in endless reasoning loops. In a production environment, even occasional errors can be critical failures that waste time and tokens. The problem isn't the ReAct pattern itself. The problem is that we are forcing the LLM to orchestrate low-level, predictable logic that should be deterministic code. We got tired of paying the "reasoning tax" for sub-routines that don't need it. So, we built Sparsi —a framework for shifting complex logic out of your ReAct agent's prompt and into deterministic "Macro-Tools" built as DAGs (Directed Acyclic Graphs). The Macro-Tool Pattern There are two ways to use Sparsi: as an end-to-end solution for a specific task, or to create higher-level tools that plug into your existing agents. The latter is where the magic happens. Instead of giving your ReAct agent 10 tiny, flaky tools and hoping it chains them correctly, you build one highly reliable, deterministic Sparsi DAG to handle that specific sub-routine. You then expose that DAG to your agent as a single Model Context Protocol (MCP) tool. The overall agent still drives the conversation, but it delegates the heavy lifting to a reliable macro-tool. We chose the DAG architecture for three main reasons: Deterministic & Testable: The graph is made of plain code. You only run AI where natural language understanding is strictly required. Parallel by Architecture: Independent branches run co

Kubernetes changed infrastructure forever with one idea: you declare desired state, and the system continuously reconciles reality to match it. I wanted that for my codebase, so I built Claude Basecamp and I'm open-sourcing it today. If you're running Claude Code across more than one repo, I'd genuinely love for you to try it, break it, and help me build it out. Try it in one command npx claude-basecamp No install, no database, no config. It discovers the projects Claude Code already knows about and opens at http://localhost:4747 . Runs on macOS, Linux, and Windows. What it does Standing checks, the reconciliation loop. Declare what must always be true, and Basecamp holds it: tests always green -> runs your suite on a cadence; failures dispatch a fix run that commits dependencies current -> npm outdated; safe updates applied, majors escalated to you issue backlog triaged -> gh-powered labeling and stale-closing anything in plain English -> "the README documents every CLI flag" checked read-only, fixed on drift Checks run against deterministic local facts (your real test suite, real npm outdated) wherever possible, zero tokens spent checking. Drift launches a bounded, budgeted, approval-gated convergence run. Repeated failure escalates to a decision card on Home instead of retrying forever. Reflexes, an immune system for your AI. Basecamp mines every transcript for the moments you pushed back (interruptions, "no, don't", permission denials) and turns each into an antibody. Once armed, every Claude Code session on your machine consults that memory before every Bash/Write/Edit action, so a mistake made twice gets blocked machine-wide before it happens a third time. Session Rescue. Notices when a Claude Code session died mid-task and lets you resume the actual dead session, same session ID, full context, as a background run that finishes the job and commits. A persistent manager for every repo. Each project gets an agent with full Claude Code tools plus control over Bas

When a CLI becomes useful, someone eventually tries to automate it. That is where a progress bar can quietly become a problem. For a person, this kind of output is helpful: Translating markdown files 12/40 30% docs/intro.md It tells me that the command is alive, how far it has moved, and which file it is working on. But when another system starts reading that same output, the progress bar stops being only a user interface. It becomes an accidental contract. That was the real problem behind one of the changes in Co-op Translator v0.20.0. The release added a Rich-powered CLI progress UI, but it also added structured translation events. At first, those may look like two separate improvements. They are really two surfaces for the same state: Rich output gives a person something readable, while structured events give integrations something stable. This article is about three things: First, why console output is tempting to parse. Second, how Co-op Translator separated the Rich UI from the event stream. Third, why that separation matters for CLI, Python API, MCP, and product integrations such as Localizeflow. The problem appears when logs become state Console output is written for people. When I run a translation command, I want a quick answer to a few practical questions: Is the command still running? Which stage is active? Which file is being processed? How much work is left? Did anything fail? A progress bar is good for that. It compresses the state of the run into something I can scan quickly. But a product integration needs a different kind of information. Imagine Localizeflow running Co-op Translator as part of a larger workflow. It does not only need to know that text was printed. It needs durable state: Which translation job started Which target language is active Which stage is running Which file completed Which file failed How many items are done Whether the run succeeded If all of that only exists inside console text, the integration has to parse human language

I run a small AI trends site, and three weeks ago I started doing something simple: every day, snapshot the star count of every repo that crosses my GitHub trending scan for AI. No judgment, no curation, just append-only rows in a database. 611 repos and 2,671 data points later (June 19 to July 10), the picture of what's actually growing looks pretty different from what my feeds told me was hot. Here's what the data says. Before publishing this I re-checked every number below against GitHub's live API. Star counts drift by the hour, so treat them as of July 10. The top 10 risers, by raw stars gained Repo Gained Window From → To calesthio/OpenMontage +30,253 21 days 5,899 → 36,152 DeusData/codebase-memory-mcp +20,483 19 days 7,516 → 27,999 mattpocock/skills +19,053 15 days 137,485 → 156,538 obra/superpowers +16,887 20 days 232,908 → 249,795 NousResearch/hermes-agent +14,896 21 days 197,297 → 212,193 Panniantong/Agent-Reach +14,334 14 days 34,780 → 49,114 usestrix/strix +13,243 12 days 26,363 → 39,606 addyosmani/agent-skills +12,685 21 days 63,156 → 75,841 asgeirtj/system_prompts_leaks +11,720 21 days 43,415 → 55,135 msitarzewski/agency-agents +11,055 10 days 118,241 → 129,296 Windows differ because I only hold snapshots for the days a repo appeared in my scan; each row states its own real window. Three things in this data genuinely surprised me. 1. "Skills" are eating agent frameworks Four of the top ten are not agent frameworks. They are collections of packaged expertise that plug into an existing agent: obra/superpowers (still compounding at roughly 840 stars a day on a 250k base), mattpocock/skills, addyosmani/agent-skills, msitarzewski/agency-agents. A year ago this table would have been full of new frameworks. Now the framework layer looks settled and the growth is in what you load INTO the agent. The moat moved from orchestration code to encoded judgment. 2. The sharpest climbs are applications, not infrastructure The steepest sustained climb from a newcomer in

Building Educational Software for Mandarin Chinese and Interlingua IALA Language-learning software is most useful when it makes structure visible. I’m Ian Blas, a developer based in Buenos Aires, Argentina, and I build educational tools around Mandarin Chinese, Interlingua IALA, etymology, morphology, writing systems, and open-source language learning. Two projects, one educational approach My work currently takes two complementary forms. Chety is an educational app for Mandarin Chinese. It approaches characters and words through their structure, etymology, morphology, historical development, and use in context. Schola Interlingua is a free, open-source learning platform for Interlingua IALA. It brings together lessons, readings, review tools, and progress-oriented study on multiple platforms. The languages are different, but the design question is similar: how can software help a learner notice the patterns that make a language readable and memorable? Learning through structure For Mandarin Chinese, a character is not only a unit to memorize. It can open a path into components, historical forms, pronunciation, word formation, and reading. That perspective guides Chety’s tools for exploring characters and vocabulary. For Interlingua IALA, the focus shifts toward transparent vocabulary, reading, morphology, and sustained practice. Schola Interlingua is designed to make that learning path approachable without separating learners from the materials and tools that support it. In both projects, the goal is practical: make language learning more legible. Etymology and morphology are useful when they give learners better ways to connect forms, meanings, and usage. An open educational practice I care about software that can be examined, shared, and improved. Schola Interlingua’s development is available through its GitHub repository , and my broader work can be found on GitHub . I also write and share updates through Medium and Substack . Explore the projects Chety — Chines

When you're running maintenance across several WordPress sites in sequence, a list view with text-only status doesn't make "which site is being processed now" or "which ones are already done" easy to spot at a glance. A client put it plainly: " Make it visually obvious in the list which sites are in maintenance and which are finished. " A colored border is the obvious move, but there are real choices to make. What colors? Where do we get the state from? When does the "done" mark go away? And — can we ship this without touching the backend? This post walks through those four calls and the minimal frontend-only implementation we landed on. Color picking — "red flashing" was the first thing we ruled out How do you make the running site stand out? The intuitive answer is "blinking red," but that got cut early. Multi-site maintenance runs are long . Having something blink red somewhere on screen the whole time is a fatigue source. We went with "a gentle blue pulse + a solid green border" instead: Running : blue #2563eb border + a soft pulsing box-shadow (2.2s ease-in-out) Done (within 24h) : green #10b981 solid border + a faint inset shadow @keyframes site-running-pulse { 0 %, 100 % { box-shadow : 0 0 0 0 rgba ( 37 , 99 , 235 , 0.4 ); } 50 % { box-shadow : 0 0 0 6px rgba ( 37 , 99 , 235 , 0 ); } } .site-running { border-color : #2563eb !important ; animation : site-running-pulse 2.2s ease-in-out infinite ; } @media ( prefers-reduced-motion : reduce ) { .site-running { animation : none ; } /* respect OS-level reduced motion */ } .site-completed { border-color : #10b981 !important ; box-shadow : inset 0 0 0 1px rgba ( 16 , 185 , 129 , 0.25 ); } The prefers-reduced-motion: reduce rule stops the pulse for users who have reduced-motion enabled at the OS level (often people with vestibular sensitivity). If you're adding motion to grab attention, this is essentially required. Zero backend changes — reuse the existing log stream To tell the list UI "this site is being processed

Writing changelogs is one of those developer tasks that sounds simple until you are staring at a messy commit history. Some commits matter to users. Some are internal cleanup. Some are merge commits. Some are meaningful only if you already know the codebase. I built a small Python example that turns commit messages or git diffs into structured changelog JSON using Telnyx AI Inference. Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/changelog-generator-python What it does The Flask app exposes: POST /generate POST /generate/from-diff GET /changelogs GET /changelogs/<id> GET /health POST /generate accepts a list of commit messages: { "version" : "v1.4.0" , "repo_name" : "billing-service" , "commits" : [ "feat: add Stripe webhook retry with exponential backoff" , "fix: correct tax calculation for EU VAT exemption" , "docs: update API reference for invoice endpoint" ] } The app asks Telnyx AI Inference to return grouped changelog JSON with sections like: Features Bug Fixes Improvements Breaking Changes Documentation Other There is also a POST /generate/from-diff endpoint if you want to summarize a git diff instead of commit messages. Why structured output matters For a changelog tool, plain text is useful, but structured output is more flexible. If the response comes back as JSON, you can: render it in a docs site save it in a release database post it into a PR comment send it to Slack open a release-note review workflow let a human approve it before publishing The example stores generated changelogs in memory and gives each one an ID, so you can list recent changelogs or retrieve a specific one. Run it Clone the examples repo: git clone https://github.com/team-telnyx/telnyx-code-examples.git cd telnyx-code-examples/changelog-generator-python Create your .env file: cp .env.example .env Add your Telnyx API key: TELNYX_API_KEY=your_telnyx_api_key AI_MODEL=moonshotai/Kimi-K2.6 HOST=127.0.0.1 Install and run: pip install -r requirements.txt python app.py

The dirty secret of online dev tools Every dev tool lives on a different website. jwt.io for JWT decoding. regex101 for regex testing. Some random site for JSON formatting. Another for diff checking. Another for curl → code. Another for SQL formatting. You end up with 10 bookmarks, 10 different UIs, and 10 different servers that just received your most sensitive data — and you never think twice about it. I didn't either. Until I did. What actually happens when you use these tools Let's take jwt.io as an example. Your JWT contains: Your auth algorithm Your user ID Your roles and permissions Your token expiry Sometimes your email, name, org ID When you paste it into jwt.io — it hits their server. It's in their request logs. Maybe forever. The same goes for regex101. Your regex patterns often encode business logic — validation rules, data formats, internal naming conventions. That goes to their database. And every online JSON formatter, diff checker, SQL tool, .env checker you've ever used? Same story. You're not just sharing data. You're sharing the shape of your system. Most of the time nothing bad happens. But "most of the time" is a terrible security posture for a developer who knows better. I got tired of it The more I thought about it, the more it bothered me. I was pasting production JWTs. Real API keys. Actual .env files with database URLs. Into random websites I knew nothing about. So I built DevTab - devtab.in One tab. 110+ dev tools. Zero server calls. Everything runs 100% in your browser via client-side JavaScript and WebAssembly. Open DevTools → Network while using it. Nothing fires. That's not a marketing claim. It's verifiable in 10 seconds. What's inside JSON tools JSON formatter & validator — real-time, error highlighting with line numbers JSON minifier JSON stringify & parse JSON → TypeScript / Pydantic / Go / Zod types Auth & security JWT decoder — header, payload, expiry countdown, issued-at in human time. Zero network requests. .env diff checker —

Most apps that grew an "AI" feature in the last two years talk to one of a handful of cloud APIs, and almost all of them speak the same dialect: the OpenAI Chat Completions format. That one detail is the reason you can pull the cloud out and run the whole thing locally on a Mac without the app ever noticing. Here is the trick, why it works, and the gotchas that bite. The one interface everything agrees on OpenAI's /v1/chat/completions endpoint became the de facto standard. So when an app lets you "use your own key" or "set a custom base URL," it is almost always going to POST to {base_url}/chat/completions with a JSON body of messages and read back the same shape. It does not care what is on the other end, only that the response matches. Local runners leaned into this. Both popular Mac ones expose exactly that endpoint: Ollama serves an OpenAI-compatible API at http://localhost:11434/v1 (its native API lives on /api , but the /v1 path speaks the OpenAI dialect). LM Studio has a built-in server you switch on from the Developer tab, serving on http://localhost:1234/v1 . So "make this app local" usually reduces to: point its base URL at one of those, put any non-empty string where it wants an API key, and pick a model you have pulled. The 60-second version Ollama: brew install ollama # or the .dmg from ollama.com ollama serve & # server on :11434 ollama pull llama3.1:8b # pull a model once Confirm it speaks OpenAI: curl http://localhost:11434/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "llama3.1:8b", "messages": [{"role": "user", "content": "say hi in 3 words"}] }' If that returns a choices[0].message.content , any OpenAI-compatible client can use it. In the app, set: Base URL: http://localhost:11434/v1 API key: ollama (or literally anything; it is ignored) Model: llama3.1:8b LM Studio is the same idea with a GUI: load a model, toggle the server on, and use base URL http://localhost:1234/v1 . Pointing real tools at it The pattern shows up

OpenAI's new family of models will continue to power Microsoft's suite of workplace and productivity apps.

The new exchanged-traded funds exclude companies that are founded, controlled, or led by Elon Musk. That means no SpaceX or Tesla.

Microsoft may once again be struggling to keep up with its own climate goals, according to its 2026 sustainability report. As reported by GeekWire, the report states that Microsoft's carbon emissions increased 25 percent in 2025, totalling 34 million metric tons "without select interventions." Microsoft says this was "driven primarily by the expansion of our […]