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Six months ago I wrote up my local AI setup and a reader bookmarked it, tried to reproduce it last week, and emailed me confused because half of it no longer matched what I actually run. Fair. Stacks rot quietly. So here's the mid-2026 state of mine: what's still earning its place on disk, what I deleted, and where I quietly went back to the cloud. Context for the numbers and opinions below: I do smart contract security work, I run everything on WSL2 on a machine with a modest GPU, and I've been doing the local-model thing daily for over a year, not as a hobby but as part of shipping. Still here: Ollama as the runtime Ollama remains the center of the local stack and honestly it's not close. I've tried the alternatives, llama.cpp directly for control, a couple of the newer serving layers for speed, and I keep coming back for one boring reason: the API is stable and everything I've built talks to it. My audit tooling, my shell scripts, my editor config, they all point at localhost:11434 and they've pointed there for a year without breaking. That stability matters more than a marginal tokens-per-second win. When a model update lands, ollama pull and I'm done. The day something meaningfully better appears with the same API shape, I'll switch in an afternoon, which is exactly the position you want to be in. Still here: qwen2.5-coder, both sizes, different jobs I run two models and the split has stayed remarkably stable: qwen2.5-coder:1.5b is the reflex model. It handles anything where speed matters more than depth: quick "what does this diff do" summaries, commit message drafts, pre-filtering files before a heavier pass, and the small classification jobs inside my pipelines ("does this file handle user input, yes or no"). It's fast enough on my machine that I never think about invoking it, and that's the whole point. A model you hesitate to call is a model you stop calling. qwen2.5-coder:7b is the thinking model. Code review, security triage, structured findings extracti

Microsoft is on a mad dash behind the scenes to patch exploits before hackers find them.

Hey everyone, Whenever I start scaling out a new Rust service or protocol, I always find myself hitting the same wall: testing gets messy fast. You end up juggling basic cargo test unit checks, hacking together ad-hoc integration scripts, and manually setting up coverage tools every single time. I put together RustForge to solve that headache for my own projects, and figured it might save a few of you some time too. It’s a clean, zero-bloat starter template designed to take you from simple unit tests all the way to compiler-style UI snapshots and coverage tracking without having to reinvent the harness every project. https://github.com/rwilliamspbg-ops/RustForge

AI agents often begin as application code: a prompt, a model call, a few tools, and enough control flow to make the first example run. As the agent grows, the definition and the environment tend to collapse into each other. Model configuration lives beside credentials. Tool access is mixed with prompts. Persistence assumes a particular process. Deployment choices become part of the agent itself. That coupling makes an agent harder to inspect, test, move, and recover. We built Clear Ideas Agent Runtime around a different boundary: the agent definition should be portable, while the infrastructure that executes it should remain under host control. The Agent Manifest is the portable contract An Agent Manifest is a versioned YAML or TypeScript definition. It can describe: prompts and structured outputs; typed variables; conditions and loops; tools and MCP connections; approvals and webhooks; sandboxed code steps; sub-runs; limits and final outputs. A separate Agent Run Manifest supplies the values and execution choices for one invocation. That keeps the reusable agent definition distinct from the inputs and operational decisions associated with a particular run. Here is a small example: schemaVersion : " 1.0" name : research-brief variables : topic : type : string researchNotes : type : string briefDraft : type : string steps : - id : research type : prompt prompt : | Research {{ topic }} and return concise notes. outputVariable : researchNotes - id : draft type : prompt prompt : | Draft a brief using these notes: {{ researchNotes }} outputVariable : briefDraft The manifest describes the agent. It does not contain the credentials, infrastructure account, or persistence implementation that happens to run it. The host controls the operational boundary The host application supplies and controls: models and provider credentials; connections and tool authorization; persistence and artifact stores; local or remote compute; sandbox providers; telemetry; concurrency and resource

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X sued the WFA in 2024 for conducting what it called a "systematic illegal boycott" of the platform after it saw a decline in advertising revenue following Musk's $44 billion takeover of the social network

If your training dashboard starts as one HTTP request and grows into athletes, activities, wellness, workouts, gear, and performance data, a hand-written fetch wrapper becomes expensive to maintain. Every new endpoint adds another URL, another response shape, and another place to get authentication or retry behavior wrong. This tutorial shows a small, reproducible path with intervals-icu , an open-source TypeScript client for the Intervals.icu API . The goal is not to build a complete training application. It is to establish a typed client, choose the right authentication boundary, call one service, and understand what changes when you move from version 1 to version 2 of the library. TL;DR Install the stable npm package, create an IntervalsClient with an API key or OAuth access token, and use service accessors such as client.athletes or client.activities. Version 2 uses typed service methods, retries selected transient failures, and defaults requests to the authenticated athlete. Prerequisites You need: Node.js 18 or newer. npm. An Intervals.icu account with an API key, or an OAuth access token for an application acting for other users. A TypeScript project that can run ESM modules. The published package is intervals-icu version 2.2.1, and its package metadata declares Node.js >=18.0.0. The repository is public and licensed under MIT. The examples below target that stable package version, not an unreleased default-branch change. Install the stable client Create a small project and pin the package version used in this tutorial: mkdir intervals-demo cd intervals-demo npm init -y npm install intervals-icu@2.2.1 npm install -D typescript tsx The package publishes both ESM and CommonJS entry points and exposes TypeScript declarations from its package root. Add a script so a .ts file can run without a separate build step: { "type" : "module" , "scripts" : { "start" : "tsx src/index.ts" } } Create the smallest useful client Create src/index.ts. Keep the credential outside

Missing data is not the same thing as a convenient default. That sounds obvious, yet temporal software regularly converts an empty time field into midnight, noon, the current time, or the start of a day. The interface may look complete after that conversion, but the program has silently changed an unknown fact into a known one. This matters anywhere an hour can change the result: medical timelines, transport schedules, legal deadlines, astronomical calculations, historical records, and calendrical systems. I encountered the problem while working with a BaZi calculation pipeline. A BaZi chart can use year, month, day, and hour components. If the birth time is absent, the honest result is a three-component analysis with hour-dependent conclusions withheld. Inserting noon would make the output look richer while making its provenance weaker. The useful engineering question is not “Which fallback time should we choose?” It is “How do we keep uncertainty visible through every layer of the system?” The public calculation evidence repository provides the concrete calendar-domain fixtures referenced below. The rest of this article focuses on the reusable software boundary behind them. Model knowledge, not just a string A common input model makes absence too easy to erase: const birthTime = form . time || " 12:00 " ; After this line runs, downstream code cannot tell whether noon came from the user or the fallback. Validation, analytics, caching, and the result renderer all see the same string. The information loss happens before the calculation begins. A small discriminated union keeps the two states separate: /** * @typedef {{ kind: "known", localTime: string, source: "user" }} * KnownTime * @typedef {{ kind: "unknown" }} UnknownTime * @typedef {KnownTime | UnknownTime} BirthTime */ function parseBirthTime ( value ) { const normalized = value ?. trim (); return normalized ? { kind : " known " , localTime : normalized , source : " user " } : { kind : " unknown " }; } This typ

AI home management startup Hint, co-founded by Martha Stewart, wants to become an “AI for your home,” combining property records, maintenance schedules, home documents, and an AI assistant into a single app.

It's a widely held belief that restarting your phone improves performance. Here's the truth.

Just two months after a bumpy launch earlier this year, Ferrari has reportedly already hit its 2026 sales goal for its polarizing, Jony Ive-designed Luce EV. Ahead of Ferrari announcing its quarterly earnings on Thursday, the Financial Times reports that Ferrari was aiming to sell "just under 500" Luce units this year, and reached that […]

Welcome to ForkMesh World Most developer tools start with another dashboard. We started with a beach. Not because developers desperately needed virtual sand, but because software is built by people, and people spend way too much time staring at rectangular windows. We're building ForkMesh World , a place where developers, open-source communities, and companies can actually hang out while building software. Not another Slack clone. Not another Zoom call. Something that's actually fun. You finish reviewing a pull request. Instead of closing your laptop, you walk outside your team's office. Someone is flying a drone over the island. Another team is racing cars down the road. A few contributors are hanging out on the beach after finishing a release. Someone jumps off the roof because... honestly, why not? (Don't try that in real life. Gravity has terrible UX.) This isn't replacing Git. It's making the community around Git feel alive. Your own office Every company and open-source project can have its own space inside ForkMesh World. Think of it as your team's home. A place for: Team meetings Community events Contributor onboarding Product demos Hackathons Launch parties Casual conversations Instead of sending someone a Discord invite and six documentation links, imagine saying: "Come by our office." Built for developers ForkMesh World is part of the larger ForkMesh ecosystem. ForkMesh is our open-source federated Git platform that lets developers own and preserve their repositories across a network instead of depending on a single hosting provider. We're trying to make developer infrastructure more resilient, while also making it a little more fun. Because open source shouldn't feel like filling out tax forms. More is coming We're only getting started. Some of the things we're working on include: 🏢 Company offices 🏖️ Beaches 🚗 Cars 🚁 Drones 🪂 Rooftop jumps (because games should be fun) 🎉 Community events 💬 Developer meetups 🛠️ Interactive spaces for open-source projects

"I'm beginning to suspect we are not in Jumanji."

A lot of us here at The Verge are fans of Remarkable’s digital note-taking tablets, and right now one of its higher-end options is much cheaper than usual. The Remarkable Paper Pro normally sells for $629 on its own, but Woot is currently offering a refurbished bundle for $449.99 through July 31st. It includes the […]

When building local RAG (Retrieval-Augmented Generation) applications, edge agents, or serverless AI pipelines, developers usually hit a wall with standard vector stores: memory overhead. Running a dedicated vector database locally often demands hundreds of megabytes—or gigabytes—of RAM just to keep indices warm. On the flip side, lightweight local options like scanning raw JSON files or querying SQLite don't scale well when vector dimensions climb into the thousands (1536d+). We built Bitweave to solve this exact trade-off: a zero-copy, SIMD-accelerated hybrid retrieval engine in Rust (with Python bindings) that handles categorical filtering and vector search while locking its active heap footprint under 1.1 MB RSS. The Architecture: How Bitweave Achieves Sub-Millisecond Speed at <1.1 MB RAM Bitweave relies on a 3-part design to maximize search speed while keeping memory consumption negligible: [ Categorical Filters ] ---> Bit-Sliced Bitmaps │ ▼ [ Query Vector (1536d) ] --> 1-Bit SIMD Pre-Filtering (Hamming Distance) │ (Top K Candidates) ▼ [ Raw Embeddings Buffer ] -> Zero-Copy Float32 Rescoring (exact_rescore=True) │ ▼ Top-K Results Array (NumPy) Zero-Copy Memory Mapping (memmap2) Instead of deserializing index files into Python RAM or Rust heap space, Bitweave uses memory-mapped files (.bweave). The operating system's page cache handles lazy loading of index segments directly from disk into virtual address space. As a result, the active RSS memory footprint remains static around 1.1 MB, whether your index holds 5,000 or 200,000 records. 1-Bit Vector Quantization & SIMD Hamming Distance High-dimensional float32 vectors (1536d) are quantized down to 1-bit sign masks (where values > 0 map to 1 and <= 0 map to 0). During pre-ranking, Bitweave uses SIMD bitwise XOR and POPCNT operations to compute Hamming distances across candidate vectors in microseconds. Zero-Copy 2-Pass Float32 Rescoring (exact_rescore=True) Quantization speeds up initial candidate selection, but f

Polar has come out with an AI-first browser aimed at knowledge workers, and it has now raised a $5.7 million seed round led by Madrona.

OpenTelemetry for production AI applications Discussion | Link

The startup analyzes calls, messages, and CRM data to identify effective sales techniques and turn them into playbooks for AI agents.

With the Light Phone, Kaiwei Tang and Joe Hollier have spent over a decade exploring the value of simplicity in our relationship to technology, partnering along the way with players like Andrew Yang, Kendrick Lamar, and Pete Davidson. Now, with a new flip phone and a growing wave of “attention activists” pushing back against Big Tech, they think the rest of […]

GitHub pull requests in your Mac's notch Discussion | Link