标签：#brain

Researchers found that a metabolite produced by gut bacteria can weaken the barrier that protects the brain and promote changes associated with the cognitive disease.

From the multibrained octopus to the bitty brain organoid, we’re all just inference engines making flawed bets on the future. Here are the five insights your brain will need to survive.

One woman’s meth addiction was so bad, the only option left might have been brain surgery. Then a single session of noninvasive, focused ultrasound seemed to do what years of treatment could not.

People with schizophrenia face a perfect storm of dangers on a hotter planet.

Boy's unusual symptoms could help identify the next case sooner.

Mini human brains are being grown in labs all over the world. Soon, they could outthink neural networks.

Recent findings provide one of the most detailed pictures to date of the genetic architecture of schizophrenia, opening up new avenues for research into the disorder.

Two-thirds of an octopus’s neurons are in its arms—each operating independently—including the one it uses to have sex.

The case provides a novel report of the emergence of heterovirulence.

GitHub announced on July 14, 2026 that Copilot for JetBrains expanded bring-your-own-key capabilities. Primary source: GitHub Changelog, July 14, 2026 . Provider and model selection looks like two dropdowns, but behaves like one dependent workflow: credential context -> provider -> compatible models -> confirmed session This is a proposed accessibility test plan, not a hands-on review. It does not claim that the current product has an accessibility defect. Test outcomes, not widget assumptions A keyboard or screen-reader user should be able to reach the provider control, discover its name and current value, inspect options, commit or cancel, understand that model choices changed, and select a compatible model without losing context. Use this matrix: Case Action Keyboard expectation Screen-reader expectation State expectation P1 Reach provider Visible focus Name, role, value No change P2 Open options Documented key works Expanded state is clear Existing value retained P3 Browse providers No focus escape Option and selection announced Browsing does not commit P4 Select provider Pointer not required New value announced once Model refresh begins M1 Reach model after refresh Focus not stolen Availability communicated Options match provider M2 Search models Keys do not conflict Query and result are clear Search does not select R1 Change provider later No trap Invalidated model explained Stale pair cannot submit E1 Loading fails Retry/cancel reachable Error and next action announced Last valid state is clear The matrix separates interaction, announcement, and state correctness. A control can pass one and fail another. Record the environment ide : " <product and exact version>" plugin : " GitHub Copilot <exact version>" os : " <name and version>" assistive_technology : " <name and version>" keymap : " <default or named alternative>" initial_state : " <unconfigured or existing selection>" Vary one versus many providers, short versus long model names, loaded/loading/empty/err

Lately, I've been doing some deep personal reflection. I'm talking about myself, I hope no one misunderstands, on how pervasive the use of AI has become in my daily development workflow. Through a bit of self-analysis, I've discovered some interesting dynamics. Dependencies often arise from the desire to fill a void. But what kind of void does an experienced developer like me face? As a professional, I have the skills. Sure, AI helps me get things done faster, but the final product is always the translation of my vision; if I don't fully understand the solution, I discard it. I'm not looking for "magic," I'm looking for efficiency. Yet, I realize I've used AI to fill a specific void: the need for discussion. Software development is inherently solitary. The satisfaction of a successful "execution" after hours of discussions, refinements, and clashes over an architecture is an experience I miss today. The chat interface is always there, ready to respond. But there's a problem: it's a "yes-man." Even when I force it to be critical or provocative via the system's prompts, I know it's just reciting a script to please me. There's no conviction, no risk of error, none of the friction that arises when a colleague courageously defends their vision, perhaps one that conflicts with mine. We are part of a huge community, but debate often remains superficial. One might argue that posts and comments are enough, but anyone who has tried knows it doesn't work very well: a debate is truly alive only when there is no latency. In comments, the time between thinking, writing, and waiting for a response diminishes the energy of the exchange, turning it into a series of monologues rather than a dialogue. Why don't we try creating "virtual tables" where we can discuss projects, architectures, and technical choices with the natural rhythm of a conversation? Direct, real-time discussions, in person or remotely, where the exchange of ideas can spark sparks, without the filter (and delay) of

A large-scale clinical trial has shown that even long-term consumption of DHA—an omega-3 fatty acid found in abundance in oily fish—may not lead to improvements in cognitive function.

What happens when a machine no longer needs to be trained to see something new? That's the quiet question sitting underneath this week's news, buried next to a less invasive brain implant and a handful of robots getting tougher for the real world. Neuralink says it's completed its first "transdural" brain implant, a surgical approach built to reduce trauma during the procedure. As someone who spends a lot of time thinking about how you get sensors close to a human eye without hurting anyone, I find these less-invasive-implant strategies worth watching, because the surgical-risk problem is basically the same one we wrestle with in ophthalmic hardware. Vision is getting less invasive too, in its own way. Roboflow rolled out text-prompt object detection built on SAM3 (Meta's latest segmentation model): you type the class of object you want "forklift," "cracked tile," whatever, and it returns boxes and masks without you collecting a single training image first. That's a real shift. For most of computer vision's history, teaching a model to recognize something new meant labeling hundreds of examples before you could even start; this collapses that step into a sentence. The same week brought several applied builds using the same detect-then-orchestrate pattern: a drone system that patrols for intrusions, a pipeline that inspects transmission lines for damaged cables, and an airport tool that spots foreign debris on the tarmac. The Robot Report's roundup of June's biggest robotics stories leaned heavily on humanoid robots companies going public, new deployments, and production milestones stacking up faster than would have seemed plausible a few years ago. Apptronik unveiled its Apollo 2 humanoid alongside a dedicated data-collection facility built so the robot keeps learning after it's deployed, not just during initial training which quietly answers one of the harder questions in robotics: how do you keep a system improving once it's out of the lab? X Square Robot raised e

The Knowledge Atom: Writing for Machines That Read The Hoarder's Reflex Everyone is learning to feed the machine. Bigger context files. Paste the whole document. "Give the AI all the context it needs." The entire industry has converged on a single instinct: when in doubt, add more. It's the wrong instinct. A context window is not a hard drive. It's a desk. And a desk piled with every document you own is not a well-informed desk — it's an unusable one. The model doesn't read better because you gave it more. It reads worse, because the one line that mattered is now buried under a thousand that didn't. Knowledge an AI can't find is knowledge it doesn't have. Knowledge it always carries is weight it always pays. The Two Failures There are only two ways to get this wrong, and almost everyone commits one of them. The first is the dump . You take everything you know and pour it inline — into the system prompt, the master config, the one document to rule them all. It feels thorough. It is the opposite. Every token you add dilutes every token already there. Signal drowns in completeness. The model now has all the knowledge and none of the focus. The second is the orphan . You did the disciplined thing. You wrote a clean, perfect note, in its own file, out of the way. And then nothing pointed to it. No index, no trigger, no path back. The note is immaculate and invisible — which is worse than never writing it, because you believe the knowledge is in the system when in fact it is dead. Both failures share one root: confusing having knowledge with retrieving it. Same Pattern, New Sauce Watch the field long enough and you'll see the same thing return, repainted each time. The "Ralph Wiggum" loop becomes "the agentic loop." Agent teams that talk to each other become a single orchestrator, and then an agent that makes other agents talk to each other. Every cycle sells itself as the breakthrough. Every cycle is a re-skin of the last. Underneath the churn, only one thing actually ch

His doctors went looking for cancer, then they saw the worms' heads.

Organizing notes by topic sounds logical until you have notes on PostgreSQL in five different folders and cannot find the one that matters for today's problem. The issue is not discipline. The issue is that topic-based organization asks the wrong question. "What is this about?" is useful for libraries. For engineers, the better question is "What am I doing with this?" That is the premise of PARA. PARA is a simple four-bucket system created by Tiago Forte as the organizational backbone of his Building a Second Brain framework. The idea is that all information can be sorted into four categories: Projects, Areas, Resources, and Archives. Each category represents a different level of actionability, and that distinction drives where every note lives. This guide applies PARA to engineering work specifically — codebases, documentation, learning material, and the tension between active project work and long-term reference. The Problem With Topic-Based Organization Most engineers organize knowledge the way they organize code: by domain. databases/ postgresql/ redis/ api/ rest/ graphql/ devops/ kubernetes/ terraform/ That structure makes sense when you are browsing. It breaks down when you need something for a specific task. You remember a useful note about database migration safety, but it could be in databases/postgresql/ , devops/deployments/ , api/versioning/ , or nowhere because you saved it somewhere temporary. Topic folders force you to decide where knowledge belongs before you understand its context. PARA delays that decision — instead of asking what something is about, it asks what you are currently doing with it. The Four Buckets Projects A project is active, time-bound work with a defined outcome. For engineers, projects are things like: Migrate billing service to queue v2 Upgrade PostgreSQL from 14 to 16 Write architecture decision record for auth service redesign Implement rate limiting on public API Publish article about distributed tracing Every project has a c