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You know that feeling when you join a new project or want to contribute to an open-source repo, and you spend the first two days just trying to figure out where everything is? I did. Every single time. Clone the repo. Open the files. Stare at 47 folders. Wonder which one actually matters. Grep for the entry point. Follow imports down a rabbit hole. Give up and ask someone. That's not learning. That's just wasted time. So I built CodeAutopsy. What it does Paste any GitHub URL. That's it. CodeAutopsy clones the repo, parses every file into an AST (Abstract Syntax Tree), traces every import and dependency, and gives you: An interactive dependency graph showing exactly what imports what The entry points — where execution actually starts A blast radius map — click any file and instantly see everything that breaks if you change it An AI-generated architectural summary explaining what the codebase does, how it's structured, and how to get started Live Health Telemetry: An Edge API that generates a live SVG health badge (A to F grade). Drop the markdown in your README once. Every time you refactor and re-scan, your badge updates everywhere instantly. My own CodeAutopsy repo just hit 99/100. Drop the markdown snippet once and forget about it. What used to take days now takes about 10 seconds. The real problem it solves Every developer has been here: You're onboarding at a new job. The codebase has 200 files. Your tech lead says "just read the code." You spend a week feeling lost. You want to contribute to an open-source project. The repo has no architecture docs. You don't know where to start. You're doing a code review on a PR that touches 15 files. You have no idea what the blast radius of those changes is. CodeAutopsy solves all three. The interesting engineering problems The hardest part wasn't the AST parsing — it was keeping it serverless without hitting Vercel's 504 timeout limits, while making the AI analysis feel instant. The Serverless Timeout Hack: Doing AST extra

Hoje eu postei sobre o Mark Zuckerberg lançar a notícia mais patética que vai cobrar 19 dólares para desbloquear o Muse Spark Pro kakakakakakaka Quem vai pagar por essa merda? Mas pensando melhor bem... Talvez eu pague Eu usei muito esse modelo como Early adopter, desde quando o motor era o Llama 3.2 e sendo inferior as outras consegui extrair escrita criativa que batia de frente com Claude em personas graças ao seu RAG no ecossistema da Meta, que tinha uma criatividade absurda quando você forçava ela a consultar as redes sociais e ver como pessoas agem e comentam, porém lançou o Muse Spark que era tipo o GPT 5.2 dos Llamas kkkkkk aí só usei para pesquisa e bem... Minha tese sobre o Muse Spark é que pra mim o problema nunca pareceu ser burrice. Parece CONTENÇÃO. Não dá vibe de modelo incapaz ou inferior. Dá vibe de modelo sendo sufocado em tempo real. Porque se você presta atenção, ele: - pesquisa rápido pra cacete (Já que cada agente pesquisa uma coisa) - alucina menos em busca (pois o modelo refina a busca dos agentes, muitas vezes consegui resultados mais confiáveis que o Gemini) - já trabalha com esquema multi-agente herdado da Manus ( o trunfo dessa IA é que diferente das outras ela não comprimi seu input, ela usa agentes para cada um pesquisar cada trecho dele, o resultado é mais completo) - acha informação boa (ela pesquisa tanto na internet quanto em grupos de Facebook ou Threads se você forçar no prompt, ou seja análises de Devs>>> Wikipédia Inclusive acredito que foi por isso que o Mark lançou o "Fórum" o app que cópia o Reddit, ele quer treinar a IA com isso, o Reddit pra mim seria a fonte perfeita pra qualquer IA se aprofundar além do que pesquisar genéricas no Google, o filha da puta do Mark é rico e filantropo e faz uma cópia só para treinar a IA dele) - conecta coisa rápido (os agentes pesquisam rápido, o modelo revisa rápido, a entrega é bem rápida e gasta bem menos tokens) Só que na hora de responder… Parece o GPT free kkkkkkk O raciocínio corta no

Hey Dev.to the community, I'm Ashwin Gururaj — a Data Scientist & AI Engineer based in Melbourne, Australia, currently open to full-time, contract, and internship opportunities. I specialise in building production-grade AI systems — not just notebooks and demos, but end-to-end pipelines that actually run in production. What I work with: Python · LangChain · LangGraph · FastAPI · RAG pipelines · pgvector · Multi-agent systems · LLMs · Groq · HuggingFace · Pydantic · Docker · Celery · Redis · PostgreSQL · Data Science · SQL · Pandas · Scikit-learn What I've built recently: Sift — an open-source multi-agent fact-checking pipeline. Takes any text, extracts every factual claim, retrieves grounded evidence via HyDE RAG + live web search, and returns auditable verdicts with cited sources. Built with LangGraph, pgvector, FastAPI, and Docker. → GitHub Open to: Full-time Data Scientist / AI Engineer / ML Engineer roles Remote or Melbourne-based Companies building serious AI products If you're hiring or know someone who is — I'd genuinely appreciate a connection. GitHub: https://github.com/ashg2099 LinkedIn: https://www.linkedin.com/in/ashwin-gururaj-93943816a/ Thanks!

I've been exploring some lesser-known languages and I'm curious what the community thinks. Languages like Go, Rust, and Kotlin have gained traction, but I feel like there are some gems that don't get enough attention. What programming language do you think is underrated? Which one deserves more love from developers? Share your thoughts and why you think that language is underrated!

🗓️ This Week Completed two more sections of the SwiftUI tutorial 🦾 As I continue working through the tutorial, I can feel my understanding of SwiftUI fundamentals becoming more solid 🔥 It was my first time posting a standalone article about reverse engineering📝 If you're interested, feel free to check it out 👇 A Curious Journey Into Reverse Engineering an AI-Generated Python .exe Umitomo Umitomo Umitomo Follow May 26 A Curious Journey Into Reverse Engineering an AI-Generated Python .exe # beginners # reversing # security # python 5 reactions Comments Add Comment 5 min read I started creating UI designs for my future portfolio website in Figma. I was able to roughly sketch out the overall structure of the site, but I also realized how difficult it is to create modern and stylish UI designs. (It really made me realize I don’t have much design sense yet 😂💦) While struggling with the design process, I came across several articles about Figma MCP . That made me interested in exploring how generative AI could help with UI design ideas, so I decided to start researching Figma MCP further. Completed Securing AI Systems room from the AI Security Learning Path on TryHackMe this week🤖 📱 iOS (SwiftUI) Worked through the SwiftUI tutorial and completed "Create an Algorithm for Badges" and "Add inclusive features" 🌐 Web Development Posted my weekly dev log on Dev.to and a standalone article about my first attempt at reverse engineering 📝 Created rough portfolio website UI layouts in Figma Used shadcn/ui component library design templates in Figma Started learning UI design in Figma using community resources 🔐 Security (TryHackMe) Completed Securing AI Systems room (part of the AI Security Learning Path) on TryHackMe. 💡 Key Takeaways 📱 SwiftUI Learning Add inclusive features Learned that SwiftUI automatically adapts UI elements for Light and Dark Mode by default. Learned how to preview and compare Light and Dark Mode layouts in the Xcode canvas. Understood that system-provided sema

Everyone can build it. Almost no one can afford to run it at scale. And the companies selling the picks and shovels are about to get undercut by the same forces they unleashed. by VEKTOR Memory — 20 min read How This Article Started: 20 Forums, 40 Headlines, and a Growing Sense That Everyone Was Confused I woke up to clear skies and the sun finally shining, and I set out to understand this idea, the truth behind it, and the nagging suspicion that the narrative around AI and software costs had become so loud, so uniform, and so confidently confusing that someone needed to sit down and actually go through it. No tweets, or are they now X's? No LinkedIn thought leader infomercials, no Substack hype, just actual research and deep thoughts. So I spent time reading, collating data. Forums, whitepapers, LinkedIn posts, Hacker News threads, VC essays, Reddit arguments. I went looking for the real signal underneath the noise. What I found instead was the full spectrum of human overconfidence, lots of moat real estate. On one end: the hype machine at full throttle. “Software is going to zero.” “A solo dev can now build what a 50-person team built in 2021.” “The era of the $500/month SaaS subscription is over.” “Vibe coding will replace your entire engineering org.” These headlines were everywhere. Breathless. Confident. Shared tens of thousands of times, this angle gets views, of course, the algorithm loves being fed claps, shares, comments, and reposts. Most were written by people who had a very good Tuesday with Codex, Windsurf, Claude and Cursor and decided that instant dev, open source to Github and getting oodles of stars, maybe even roping in a celebrity, was now the permanent condition of software development. “We are now famous on GitHub!" Very hipster, very vibes, see you on the playa.. On the other end: the backlash. Experienced engineer, people with 15 to 25 years in production systems are pushing back hard. “Show me the vibe-coded app that survived its first real

A new law and AV tracker tool gives the clearest accounting yet of how many robotaxis and self-driving trucks are in Texas.

We were burning 400ms in p99 tail latency on a core event-processing path in Veltrix. The upstream teams kept blaming the network, but the numbers didnt lie—64% of the time was spent inside the JVM, specifically in sun.misc.Unsafe.park during GC pauses. Every time we hit 80% heap pressure, the throughput collapsed and we lost 300k events per minute. That was the exact moment I stopped believing in the JVM as the runtime and started looking at the system boundary. The first attempt was aggressively tuned HotSpot with G1GC and pinning the critical threads to their own NUMA nodes. We set -XX:MaxGCPauseMillis=20 , -XX:+UseNUMA , and even migrated to Azul Zulu Prime because its handling of large heaps was supposedly better. The p99 dropped to 280ms, but the GC telemetry still showed a sawtooth pattern of 30–40ms spikes every 230ms on a 16GB heap. Profiling with JDK Flight Recorder told us 18% of CPU time was spent in card-table scanning. At that point I knew we were fighting the runtime, not the problem. The event pipeline was small—just JSON parsing, enrichment, and a single RocksDB write—but the JVMs generational collector couldnt stop moving objects. The architecture decision came during a four-day blackout window after a failed Blue-Green deploy. Three of us sat in a war room with a single Grafana dashboard showing 100% CPU steal time on the Kubernetes nodes. We had two choices: squeeze more life out of the JVM by manually balancing the heap or rewrite the critical hot path in Rust and give the compiler full control over memory layout. The Rust option meant losing the JVM ecosystem (no more async-profiler, no more one-liner heap dumps) but gave us stackless futures, zero-cost abstractions, and compile-time memory safety. We chose Rust. We forked the Cargo.toml wed used in a sidecar for metrics and started porting the event collector. The numbers after the rewrite told the story. We recompiled the same two endpoints— POST /events and GET /aggregates —and served them f

I Built a Local AI Agent That Thinks Like a Brain, Not a Database Most AI agents today are sophisticated autocomplete engines. Ask them something, they answer. Ask again in a new conversation, they start from zero. The context window is the only memory they have. Serenity is different. It's a fully local AI agent that encodes experiences the way biological brains do — semantically clustered, causally structured, and self-organizing. No cloud. No API calls to a vector database. No data leaves your machine. Ever. The Core Problem with Current AI Memory The standard approach to AI memory is essentially a hack: you stuff embeddings into a vector DB, do nearest-neighbor retrieval, and dump the results into the prompt. It sort of works. But it's not how brains work. Your brain doesn't search for memories. When one fires, related ones light up automatically. Serenity's architecture — called S.E.R.A (Semantic Experience Reasoning Agent) — tries to bridge that gap. Here's the key difference: Traditional Approach Serenity Vector search on embeddings Semantic node activation Prompt-injected context Persistent working memory One-shot retrieval Emergent recall via association Static embeddings Pruned & crystallized over time How It Works: The Neural Node Network At the core is the Neural Node Network (NNN) . Instead of storing facts in isolation, Serenity encodes experiences in causal format: ACTION → BEFORE → OUTCOME → AFTER When she learns something, she doesn't file it in a folder. She finds where it semantically belongs in a web of related concepts. Similar things cluster together — the same way neurons that fire together wire together. Then the abstraction layer kicks in. Three or more related concepts crystallize into a higher-order node: the thing they all have in common that none of them says directly. Those nodes bundle into pathways. Those pathways grow into domains. She also has inhibitors and pruning — weak connections get cut so strong ones sharpen. Her knowledge ge

Getting this 0.0.0.0 error while signing in to my website with Google oauth , any fixes ? I can still get inside if I try again but it may be irritating for end consumers submitted by /u/Haunting-Soft3896 [link] [留言]

I just need to get this out before I break my laptop in half. I have worked in UX/UI for nearly 10 years. I've used so many website builders. The worst BY FAR is Webflow. Where do I start? Very little custom code support: seriously, pretty much all styles and JS needs to be added in the site settings? SO CLUNKY. Give me page level control at the very least - I refuse to hack everything together with embeds. Insane. Components are too locked down: so I can't modify one thing (outside of properties) in a special exception? I get that's how a code base works, but you are a VISUAL designer. Be more like Figma Animations are a nightmare: why the funk do I have to define the hover out state? Are you kidding me? One hover state, animate in and out by default. Doing custom hover outs should be an exception, not the standard. I know this is the basic setup for very simple element level animations, which is crazy that they expect any web designer to use those more than multi-level animations. Also WHY CAN'T WE ANIMATE MARGIN AND PADDING??? You can't organize CMS collection content: complex data entry? Have fun scrolling through a massive list of inputs and selects with no hierarchy Naming every fudging element is hell. Can we please just switch to a css framework native approach? Just apply class names based on something like Tailwind when we use the visual editor to adjust properties. Sure, custom classnames when desired, but omg the brainpower it takes to care about classes makes me gag Along those lines: why can't we see a list of all the classes??? If everything is going to create a custom class then at least let me see a list/the CSS for the love of gourd No inline SVG element option is infuriating. Yes SVGs can be a security vulnerability but it's 2026 FIGURE IT OUT. So nasty to have to use embedded code if you want to be able to easily change the color, line weight, etc of an icon I swear who is this product even for 😭 my clients can't use it because you need to have k

OpenAI shares guidance on third-party AI evaluations, covering how to assess model capabilities, safeguards, and validity for frontier systems.

Claims about low testosterone and false accusations of veganism might play well to the online far right, but will they win an election?

Notify your AI agent when the web changes Discussion | Link

A few years ago I would've said crashes are the worst bugs. Now I think it's the opposite. The worst bugs are the ones where everything technically works. The request succeeds. The button works. The API returns 200. Nothing shows up in logs. Meanwhile users are getting confused, clicking the same thing repeatedly, or abandoning a flow entirely. By the time someone notices, it's been happening for weeks. What's the most frustrating example of this you've run into? Edit:- I am not getting the point why everyone is downvoting this post ? It's a serious question guys submitted by /u/Icy-Roll-4044 [link] [留言]

As the title goes. I'm just too f*cking slow! I'm sitting with a client's project for 6+ months (without pay) and I've covered only about 50% so far. This project is mildly complex- a custom video distribution platform with background jobs. However, have I been faster, it would have gone live by now! I took a whole lot of time understanding the technical concept, the underlying system design. Right then, I got hold of the sinister thing called procrastination, in combination with fear of failure, as well as perfectionism! The actual implementation is going even slower. I'm the perfect candidate to be replaced by AI; at least in terms of speed. I fear how will I learn and work on other technologies later on! This is also destroying my overall confidence and career. I have seen the sentence 'ship fast, polish later.' But I just won't follow it even if I go broke financially. Man, I'm fed up of myself! Has anyone ever recovered from similar situation? submitted by /u/swb_rise [link] [留言]