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Amazon has announced a new version of its fully autonomous warehouse robot, Proteus, that will can interact using language instead of code. The expanded capabilities come as part of a growing pivot toward automation as the e-commerce giant replaces its human workers with robots. Amazon says the AI-powered upgrade means its human employees can assign […]

SpaceX expects to become a trillion-dollar company when it goes public.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Automated support that learns from docs + past conversations Discussion | Link

my friend just told me about this and i had to share it immediately cursor is giving students 12 months of pro completely free. no credit card. just verify your .edu email and that’s it you get full access to gpt, claude, gemini… all the models. for a whole year. for free. that’s $240 you just keep in your pocket while everyone else is paying $20 a month wondering why their bank account looks sad takes like 2 minutes. go to cursor.com/students, throw in your .edu, pass the verification, done and if you graduated already, you probably know someone still in college who has no idea this exists. do them a favour link in the comments. seriously just go do it right now submitted by /u/NewMuffin3926 [link] [留言]

ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.

You can get a 2h free trial by solving a proof-of-work captcha when topping up your account for the first time. If you'd like to learn more, an independent interview was posted a couple of weeks ago [1], and the FAQ [2] has a lot of information as well. For the source code sharing, we've talked with lawyers and are inclined to no longer require the NDA/NCC for privacy reasons shared with us before (signing requires identification), but instead use a source-available permissive license that doesn

I wrote a short article about why Pandas is worth learning from a general programming perspective, not just a data science one. A lot of everyday programming work involves tabular data - CSV files, reports, logs, exports, billing data, sales data, inventory data, operational spreadsheets, analytics extracts, etc. You can process that kind of data with loops and dictionaries, SQL, shell tools, or spreadsheets. But Pandas gives Python a very compact and expressive way to do filtering, grouping, aggregation, joins, and reshaping in code. The article uses a small sales/purchases CSV example and compares the Pandas approach with plain Python and spreadsheet-style thinking. I’m curious how other programmers think about this: is Pandas one of the libraries that makes Python worth learning, even for people whose main work is not data science? Or would you usually reach for SQL, spreadsheets, shell tools, or something else? submitted by /u/Horror-Willingness74 [link] [留言]

Your ambient memory for Claude Discussion | Link

Initially, I developed this so I can easily switch between different Attention mechanisms for my Small Language Model (SLM) experiments and benchmarking. However, I also realized that these implementations can be applicable in Computer Vision, modernize Vision Encoders, RL, and others. I hope this helps researchers, students, or educators in general. I also included MiniMax M3's sparse attention. This can be integrated with Andrej Karpathy's autoresearch framework. For contributing: I encourage you to please open a PR. I would like to see and learn implementations of other attention mechanisms I haven't covered in this repo. Thank you! GitHub Link: https://github.com/egmaminta/attnhut submitted by /u/AnyIce3007 [link] [留言]

The company is also introducing a new age verification process in partnership with a European bank.

Slogging through emails to find stuff can be a chore, but Google AI subscribers can now get Gemini to do it.

Hi everyone! Tim here, maintainer of the lucide-motion-vue library. I build this as a way to use nice animated icons in my webapps. We were already on lucide, and found animate-ui animated icons as a great collection, unfortunately React only or made to be used with shadcn. So I ported the library to Vue, and combined it with another library (lucide-animated.com). As both libraries dont share the same animations and/or icons, this creates the largest animated icons library for vue that can be us

Got the gguf quantized version running about two hours after release and I genuinely wasn't expecting this from a 12b model. The multimodal stuff actually works, fed it screenshots of my codebase and it parsed the architecture better than most 70b models I've tested. The 256k context window is real and it doesn't fall apart at the edges like llama models do past 32k. Loaded a full repo into context, it tracked references across the whole thing. Single 3090 with q4 quantization runs at about 15 tokens per second which is totally usable for dev work. What gets me is the size range. The 12b sits in this sweet spot where you get strong reasoning without needing multi gpu. Tried the e4b on my laptop with 16gb ram, slower but functional. Already swapped it into my local coding pipeline. The function calling support means I can wire it into my toolchain without the janky workarounds I had before. Native audio input on the 12b is something I haven't touched yet but the implications for voice driven workflows are kind of insane. submitted by /u/Sharkkkk2 [link] [留言]

Bilingual post · Post bilíngue Jump to: English · Português English {#english} Codegen to C: Native Binaries from Pascal (v2.18.0) Sprint 10 ( v2.18.0 ) closes the loop on CrabPascal's most ambitious feature: turning Pascal source into real native executables via C codegen — with string builtins that actually match the interpreter. The pipeline Pascal (.dpr/.pas) → AST → C source + stubs.c → gcc/clang → native binary run skips the last steps and executes in Rust. build-exe is for when you want an .exe or ELF on disk without carrying the CrabPascal runtime as a dependency. End-to-end example program NativeHello ; uses System . SysUtils ; begin WriteLn ( Trim ( ' Hello, native world! ' )); end . crab-pascal build-exe NativeHello.dpr ./NativeHello # or NativeHello.exe on Windows Expected output: Hello, native world! — no leading or trailing spaces. What Sprint 10 fixed Parser: Trim , Copy , Length , and friends are recognized as SysUtils builtins , not mistaken for type names starting with T . A denylist prevents hard-casts that produced invalid C. Codegen: Forward declarations for pascal_* helpers in generated C. WriteLn emits correct %s formats for string expressions. main returns 0 like a well-behaved C program. Tests: build_string_conformance_stdout_matches_run_when_toolchain_present runs only when gcc/clang is available — skipping cleanly in CI sandboxes without a compiler, failing loudly when a compiler is present but output diverges. cargo test --test run_build_parity stubs.c: shared runtime surface String functions implemented once in Rust for run mirror into stubs.c for native builds: // conceptual — see repo for full signatures int pascal_Length ( const char * s ); char * pascal_Trim ( const char * s ); Generated Pascal calls route through these instead of ad-hoc inline logic, keeping Sprint 5–8 string semantics intact in binaries. When build-exe is not enough yet Sprint 10 explicitly did not ship full OO, exception, or generics codegen parity — those appear

Prompts rot. Captured failures compound. Most of the AI skills you are building are mostly prompt, which is why most of them will not survive the year. Not because the prompts are bad. A skill's value is maybe twenty percent instruction and eighty percent scar tissue, and only that second part lasts. The instruction rots the moment the thing it describes moves. Encode how your team deploys and it works until the pipeline changes. Then you are debugging a prompt at 2am, with less to go on than if you had written the script yourself. So before you build another one, stop asking whether the prompt is good. Ask what the skill is holding onto, and whether that thing sits still. A skill rots at the speed of what it touches A skill rots in proportion to how tightly it is coupled to things that move. Generic scaffolding leans on stable ground like a language or a convention, so it ages slowly. Domain logic wired to a codebase that gets refactored every quarter ages fast, no matter how good the prompt is. The difference is the dependency count. "Write a unit test in this style" depends on a language and a convention. Both barely move. It keeps working for years because nothing under it shifts. Real company-specific procedure is the opposite. File layouts. Service contracts. The one edge case in the billing flow. Each detail you pack in is a thread tied to something that gets refactored. Pack in enough of them and the skill is not a tool anymore. It is a liability with good intentions, and it fails silently, because a stale prompt does not throw. It quietly does the wrong thing. That is what the skill-library pitch gets backwards. Volume is not value. A hundred skills wired to a moving codebase is a hundred things to maintain. The only part that compounds is the scar One part of a skill does not rot. The captured failure. The five-line check you added after a model confidently reported a 41 percent dividend yield. The retry that refuses to fire twice so a flaky webhook cannot

The flagship OpenDots 2 earbuds are joined by the mid-range OpenDots Air.

Shokz has announced two new versions of its open earbuds. Like the original OpenDots One that launched in May 2025, the new Shokz OpenDots 2 and OpenDots Air are both designed to be worn clipped to the back of your ear with their drivers positioned to project sound toward your ear canals without blocking them. […]

In my previous article, I argued that AI is just the next abstraction layer — the same pattern we’ve seen a dozen times in software history. Each layer demands a new skill. So what does the AI layer demand? I think the answer is hiding in plain sight. And some very powerful people just demonstrated it. Something Interesting Happened Recently Mark Zuckerberg started coding again after a 20-year break. According to multiple reports, he moved his desk to Meta’s AI lab, spends 5 to 10 hours a week writing code, and is “coding all day long” alongside the Meta Superintelligence Labs team. The man who built Facebook in a dorm room and then spent two decades managing tens of thousands of people — is shipping diffs again. Garry Tan, CEO of Y Combinator, returned to coding after 15 years using AI tools like Claude Code. He described himself as “addicted” to it, sleeping four hours a night because he couldn’t stop building things. Sergey Brin, Google’s co-founder who stepped back from day-to-day operations years ago, came out of retirement to code on Gemini. He’s reportedly assembling an elite “coding strike team” and is directly involved in hands-on development. And there’s a quote from The New Stack that captures this perfectly: executives are building with AI because they were “tired of explaining it to somebody who was supposed to build it for me.” Why is this happening? These people haven’t written production code in over a decade. What changed? The Career Ladder Was Always About Communication Let’s take a step back. The most common career paths for a developer are either the strict technical way — from developer to tech lead, then architect — or the management way — team lead, then head of engineering, CTO. In both ways you start from doing things yourself and gradually move to teaching — or better to say, guiding — others how to do it. Or strictly overseeing the whole process. You stop writing code and start writing explanations. You stop implementing and start reviewin