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Most #cyclist# are squeezing training in around life, not the other way around. It's about stealing snippets when you can. Even just 30mins a day adds up over a few months. #persistent# training pays off 💪

Introducing Projects, a new way of working in Cursor. Rather than creating a chat for every task, you work with a coordinator agent in a single, persistent thread. Like @bot, your agent is always on, proactively manages work with subagents, and improves over time.

LATEST: 🇰🇷 The Bank of Korea raised its benchmark rate by 25 basis points to 3.00% for the second straight meeting, citing inflation above target and persistent financial stability risks.

Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL | bash Launch post: Repo: Headlong is a @LaudeInstitute / MIT collaboration.

WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓

Very welcome recent news from Signal: they are working on letting you register an account without a phone number. That said, an important counterpoint about what this would and would not accomplish. The good #1#: reducing dependence on phone numbers. Even aside from privacy benefits, reducing dependency on a highly oligopolistic system of chokepoints is good in itself. The good #2#: phone numbers are for many people not a good "root" of identity from an access control perspective. Phone numbers get sim swapped all the time. The good #3#: allowing phone-number-free accounts will make it harder for them in the future to discriminate against people by country - and so make it harder for governments to pressure them to block their own citizens. Now, on privacy. Significantly better than status quo, so yes it is good #4#, but... In practice, in 2026, I believe that pseudonymity (a long-term persistent account that is not tied to your primary identity) is a dead concept. There are just too many channels by which we accidentally slowly leak data about who we are - timing of messages, the pattern of who we send messages to with what frequency, size, etc. And too many highly effective AI-based means (both using LLMs per-user, and LLMs helping every person and agency under the sun use math that we had all along) to uncover and piece together those hints. As a trivial example, whatever server you interact with learns your IP address, but even if you hide *that* with a VPN or Tor, there are many other identity leakage vectors. And so the only defensible form of privacy is *message-by-message unlinkability* - no one except sender and receiver knows the (sender, receiver) pair, ideally even not knowing who the sender or the receiver are. A natural taxonomy of privacy is the following 2x2: * Sitting duck: adversary knows "X did Y" * Confidentiality: adversary knows "X did ???" [E2E encryption provides this] * Anonymity: adversary knows "??? did Y" [aka message-by-message unlinkability] * Ideal: adversary knows "??? did ???" Signal has already had confidentiality for a long time. (Note: in other contexts, "confidentiality" sometimes means "someone knows X did Y, and we trust that someone to not reveal it", ie. not true privacy. Here, by confidentiality we mean hiding contents from third parties) This adds pseudonymity: in the above schema, adversary knows "0x8b512c... did Y", where they don't initially know who 0x8b512c... is, but may figure that out over time. The ideal is getting to message-by-message unlinkability. Actually accomplishing that gets into territory that is currently being explored by mixnet projects as well as newer messengers, eg. @session_app and @SimpleXChat. Once we get deeper into this territory, I suspect the primary frontier will be spam and DoS protection. Right now, much of the internet blocks all Tor exit nodes - not because they personally hate privacy, but because that's where DoS attacks come from. So we need ways for people to prove their non-spammer status while maintaining message-by-message unlinkability. See here for one direction (which complements nicely). So I hope that we appreciate the victory that is mainstreaming of end-to-end encryption, that we actually get Signal accounts without phone number dependency (it's a great thing even if it had zero privacy consequences), and then that we keep moving forward and pushing the frontier of data leakage minimization.

how to give your ai agent a computer for $0 cloudflare just released computer, an open source package that gives your agent a persistent filesystem, shell access, and code execution inside cloudflare workers. no vps, no docker, no api bill what you get for free: - a linux environment with real binaries, npm and node - sqlite-backed filesystem that survives restarts, 10gb per workspace - shell execution via just-bash, no container required - built-in git client and r2 file sharing - ai sdk tools (read, write, edit, ls, exec) ready to hand to any agent setup (3 min): step 1: npm install @cloudflare/computer step 2: add withWorkspace to a durable object and set compatibility_flags to nodejs_compat in wrangler.jsonc step 3: call exec from your agent ```ts const ws = await getWorkspace(env.Agent.get(id)) await ws.fs.writeFile("/hello.txt", "world") const run = await ws.runtime.exec("cat /hello.txt") ``` use it for agents that need to read files, run commands, clone repos, build and test code, or store state between sessions no more ssh keys, no more dockerfiles, no more paying for sandbox apis

I am in Special Housing Unit (SHU). My water faucet is broken. I am not allowed bottles of water in SHU. I have nothing clean to drink. My only water comes from the shower, warm and from a brown and filthy faucet. It has given me persistent stomach problems. The water I am drinking is not clean, I am an American. Innocent until proven guilty. But I am forced to drink poisoned water. Where are my rights?

GeoLibre v2.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings a legend that writes itself from your symbology, a new GeoLens catalog browser, and 200+ GeoLibre Rust geoprocessing tools running entirely in the browser. What's new in v2.3.0 - Automatic on-map Legend: the legend builds itself from your visible layers, with class rows for graduated, categorized, rule-based, and expression styling, gradient bars for heatmaps and raster colormaps, and land-cover labels from a Raster Attribute Table. Rename, hide, reorder, or add your own entries, and it saves with the project. - Symbology swatches in the Layers panel: every row shows a dot, line, square, or image glyph in the layer's own color, so a tall layer stack reads at a glance. - GeoLens catalog browser: connect to a self-hosted GeoLens server, search its catalog, and add datasets as vector tiles, GeoJSON, or rendered raster tiles. - Emerging Hot Spot Analysis: build a space-time cube from timestamped points and classify every cell as a new, intensifying, persistent, diminishing, sporadic, oscillating, or historical hot or cold spot, all client side. - Mosaic time series: the Time Slider now steps through MosaicJSON and STAC collections of many COGs per date, on either a GPU or a WASM rendering engine. - Copy and paste layer styles: give a whole set of layers one consistent look without restyling each in turn. - Shareable tool links: deep-link any Whitebox tool with a ?tool= URL that opens the dialog preselected and pre-fills the form, with a Copy link button to build it for you. - Smarter data loading: pick which layers to load from a multi-layer GeoPackage, import CSVs whose coordinates are in any projected CRS, and read a raster's real CRS, pixel size, and extent from the metadata dialog. - Multiple AI profiles: define several provider, model, and credential setups, pick a default, and switch between them from the assistant panel. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS# #Geospatial# #OpenSource# #RemoteSensing# #MapLibre# #GeoLibre#