Hey all, we have a quick update for everyone who participated in the Google I/O Writing...
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]
AWS Face Liveness has native SDKs for iOS and Android. That works well if you are building fully...
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]
Hi! I'm having some trouble with eslint-disable comments for HTML elements defined inside a Vue SFC pug template, eslint do not recognize them and keeps throwing warnings. What I've tried so far: Comments inside the pug template, both // and //- <template lang="pug"> // eslint-disable-next-line vuejs-accessibility/no-static-element-interactions -- Standard video player click-to-play-pause behavior video(@click="togglePause" ...) </template> <template lang="pug"> //- eslint-disable-next-line vuejs-accessibility/no-static-element-interactions -- Standard video player click-to-play-pause behavior video(@click="togglePause" ...) </template> My next options are not optimal, but I ran out of ideas: A comment inside the script setup tag (it is placed before the template in the file): <script setup> //eslint-disable vuejs-accessibility/no-static-element-interactions </script> A comment at the very top of the file, before any other code <!-- eslint-disable vuejs-accessibility/no-static-element-interactions --> <script setup></script> <template lang="pug"></template> None of this worked. The only way I managed to make this work was creating overrides in .eslintrc.cjs: // Since eslint-disable comments do not work for HTML elements inside pug we // must include those overrides here. overrides: [ { // Standard video player click-to-pause behavior files: ["src/components/common/SimpleMp4Viewer.vue"], rules: { "vuejs-accessibility/no-static-element-interactions": "off" } } ] Do you know if I am missing something here? The eslint related packages I have in my projects are: dependencies "eslint-config-prettier": "^10.1.8", devDependencies "@rushstack/eslint-patch": "^1.8.0", "@vue/eslint-config-prettier": "^9.0.0", "eslint": "^8.57.0", "eslint-define-config": "^2.1.0", "eslint-plugin-unused-imports": "^4.4.1", "eslint-plugin-vue": "^9.27.0", "eslint-plugin-vue-pug": "^0.6.2", "eslint-plugin-vuejs-accessibility": "^2.5.0", Thank you! submitted by /u/bcons-php-Console [link] [留言]
AWS disclosed that Resilient Network Graphs, a flat network architecture based on quasi-random graph theory, is now the default for most new data center builds. The design replaces fat-tree hierarchies with direct ToR-to-ToR mesh connections using passive optical ShuffleBoxes, cutting routers by 69%, boosting throughput by 33%, and reducing network power consumption by 40%. By Steef-Jan Wiggers
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
Noble's FoKus Artemis headphones have three drivers and ANC, but they don't come cheap.
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]
One important concept I learned is Fanout, which is basically how posts are distributed to user's feeds. Fanout Push When a user creates a post, the system immediately pushes that post to the feed cache of all followers. This is very fast because the feed is already prepared when users open the app. Fanout Pull Instead of precomputing feeds, the system generates the feed when a user opens the application by fetching posts from accounts they follow. It saves storage and avoids unnecessary work for accounts with huge follower counts. Now real system user Hybrid Approach For normal users with a few hundred followers, Fanout Push works well because the cost is manageable and feed loading is fast. For celebrities like Virat Kohli with 250M+ followers, pushing every post to every follower's feed cache would be extremely expensive. Many followers may not even open the app, so a lot of storage and compute would be wasted. That's why large scale systems often use Fanout Pull (or a hybrid approach) for such accounts. But How Does the Feed Know to Fetch Celebrity Posts? A question I had was: If my normal friends' posts are already present in my feed cache through Fanout Push, how does the system know that it should also fetch posts from celebrity accounts? One possible approach is that the social graph stores metadata about accounts. Celebrity or high follower accounts can be marked differently. When a user opens the app, the Feed Service: Loads the feed generated through Fanout Push. Checks the accounts the user follows in the Social Graph. Identifies celebrity accounts that use Fanout Pull. Fetches their latest posts separately. Merges both results and then applies recommendation algorithms before returning the final feed. Simplified Flow User Creates Post Post Service Store in Database Fanout Service Check Social Graph & User Preferences (blocked users, muted users, close friends, etc.) Create Fanout Tasks Message Queue Fanout Workers (Push or Pull Strategy) I'm still learn
submitted by /u/DataBaeBee [link] [留言]
submitted by /u/f311a [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.