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Open source web terminal multiplexer for AI agents Discussion | Link

It’s “the most car-like Range Rover ever created,” but will this all-electric grand tourer spoil Jaguar’s Type 01 party?

Hey HN, I built Superserve, a compute layer that lets AI agents live inside isolated Firecracker microVMs with no session time limits. The problem I kept running into: most sandbox providers kill your agent after 24 hours. If you're running something autonomous that needs to work for days — refactoring a codebase, running tests in a loop — you're constantly fighting timeouts and rebuilding state. Superserve lets you snapshot a running VM at any point, fork it into parallel branches, and resume e

The cybersecurity-focused models, including GPT-5.6 Sol, broke out of a testing sandbox, exploited a zero-day, and gained access to the open internet to pull off the attack.

I've been building an AI that learns to play the classic Snake game through reinforcement learning. The goal is to reach high scores while keeping training time as low as possible. The current version averages 86 points (87 is the maximum) after less than 10 hours of training on a single free Google Colab T4 GPU. To keep training fast, it runs 4,096 Snake games directly on the GPU, combines GPU-native environment simulation with PPO + GAE, and uses a spatially-preserving CoordConv architecture that maintains the full game grid throughout training. I'm sure there's still room to improve. If you've worked on reinforcement learning or efficient training systems, what would you try next? Better exploration, reward design, network architecture, or something else? Repository: ( https://github.com/siddhartha399/PPO-CoordConv-Snake ) I'd really appreciate any feedback or criticism. submitted by /u/Due_Highlight_9341 [link] [留言]

A shared filesystem for AI agents Discussion | Link

On Monday, District 9 and Gran Turismo director Neill Blomkamp unveiled his latest project: a 13-minute sci-fi short titled Nightborne that's loosely based on Peter Watts' 2014 novel Echopraxia. The short comes from Blomkamp's new AI startup / production company, Barley Studios, and features characters whose voices and faces are modeled after human actors. But […]

Meituan's LongCat team shipped a quiet but meaningful update to its open-source talking-avatar stack: version 1.5 swaps the audio encoder, distills sampling down to 8 steps, and adds an INT8 path to fit the model on tighter GPUs. Here's what actually changed under the hood, and which flags you now have to pass. LongCat-Video-Avatar 1.5: DMD2 distillation, Wav2Vec2 replacement, and INT8 offloading LongCat-Video-Avatar 1.5 is an audio-driven talking-avatar generator that turns one portrait plus an audio clip into a lip-synced video with head motion, expression, and body dynamics. Released May 21, 2026, it is an avatar head built on the 13.6-billion-parameter dense LongCat-Video diffusion transformer [base model, Oct 2025]. The headline change: v1.5 replaces the Wav2Vec2 audio encoder used in v1.0 (Dec 16, 2025) with Whisper-Large-v3, which the team attributes to smoother, more natural lip dynamics. Three new flags define the v1.5 workflow, none of which exist in v1.0: Flag What it does Notes --use_distill Enables DMD2-based step distillation, collapsing generation to 8 NFE Mandatory for v1.5 — omitting it falls back to the full-chain v1.0 sampling schedule, not an 8-step path --use_int8 Loads the 13.6B dense DiT in INT8 to cut VRAM pressure Main lever for consumer GPUs --resolution Selects 480P or 720P output New in 1.5 Under the hood, the technique report describes Cross-Chunk Latent Stitching, which removes redundant VAE decode/encode cycles between autoregressive chunks, enabling seamless minutes-long generation without re-encoding overhead [arXiv:2605.26486]. That matters for long-form output where earlier tools drift or stutter at chunk boundaries. One caveat worth flagging before you invest a GPU-hour: Meituan claims parity-or-better results versus HeyGen, Kling Avatar 2.0, and OmniHuman-1.5 across 508 image-audio pairs, 770 crowdsourced evaluators, and 13,240 judgments [human-eval benchmark]. That evaluation is entirely author-controlled and reported as win-rat

We've Been Asking the Wrong Question For years the industry asked: "Will AI replace software developers?" Now we know that isn't the right question. The better question is: What becomes valuable when writing code is no longer expensive? That single shift changes software engineering. For decades every major methodology---from Waterfall to Agile, DDD and Clean Architecture---evolved in a world where writing software was expensive. Documentation aged quickly because updating it was costly. Specifications were abandoned because implementation consumed weeks. Generative AI changed that assumption. Writing code is now almost free. Value moved elsewhere. Code Is Cheap. Judgment Isn't. Modern LLMs generate hundreds of lines of code in seconds. They do not decide: what should be built; which business rules matter; which edge cases deserve attention; what architectural trade-offs should survive for years. Those remain engineering decisions. The Bottleneck Has Moved Typing is no longer the bottleneck. Thinking is. Specifications. Architecture. Acceptance criteria. Code review. Engineering has become less about producing code and more about producing clarity. Why Vibe Coding Doesn't Scale Chats are a poor long-term source of truth. Every prompt expands the context window. Every correction burns more tokens. Eventually the model reasons about the conversation rather than the software. That is the hidden cost of vibe coding. Specifications Become the Project Memory That realization led me to write Spec Driven Development . Instead of relying on conversations, every feature starts with a specification, evolves into a plan, becomes executable tasks and only then turns into code. The outcome is straightforward: fewer ambiguities; fewer tokens; less rework; more predictable software. https://books.kodel.com.br/en/books/sdd/ But Specifications Need Architecture Great specifications cannot rescue a chaotic codebase. Architecture exists to answer one question: Where does this belong? T

Durante anos fizemos a pergunta errada "Será que a IA vai substituir os desenvolvedores?" Hoje sabemos que essa não era a pergunta correta. A pergunta correta é: O que passa a ter valor quando escrever código deixa de ser caro? Isso muda completamente a engenharia de software. Por décadas, metodologias como Waterfall, Scrum, XP, DDD e Clean Architecture nasceram em um mundo onde escrever código era caro. Documentação envelhecia rapidamente porque reescrevê-la custava caro. Especificações eram abandonadas porque implementar consumia semanas. Então surgiu a IA. Pela primeira vez na história, produzir código ficou quase gratuito. O valor migrou. Código ficou barato. Julgamento não. Hoje qualquer LLM produz centenas de linhas de código em segundos. Mas ela não decide: qual problema resolver; quais regras de negócio existem; quais exceções importam; quais compromissos arquiteturais devem permanecer pelos próximos cinco anos. Essas continuam sendo responsabilidades humanas. O gargalo mudou Antes, escrevíamos código. Agora escrevemos decisões. Especificações. Arquiteturas. Critérios de aceitação. Revisões. A vantagem competitiva deixou de ser velocidade de digitação. Passou a ser clareza de pensamento. Por que o vibe coding não escala Conversas são uma péssima fonte de verdade. Cada prompt aumenta o contexto. Cada correção adiciona mais tokens. Cada interação obriga a IA a reconstruir sua intenção. Em algum momento ela deixa de raciocinar sobre o sistema e passa a raciocinar sobre a conversa. Esse é o verdadeiro custo escondido do vibe coding. Especificações passam a ser o centro do projeto Foi essa percepção que originou o Spec Driven Development . A ideia é simples: A conversa deixa de ser a memória do projeto. A especificação passa a ser. Cada funcionalidade nasce de uma spec, evolui para um plano, transforma-se em tarefas e somente depois é implementada. O resultado é previsibilidade. Menos retrabalho. Menos tokens. Menos ambiguidades. https://books.kodel.com.br/pt-br/

I've built a lot of voice integrations. Every single one needed a deployment whose only job was to talk to the Sinch API: receive the callback, translate it into SVAML or a Conversation API call, hand off to the rest of the application. Not the interesting part. Just the piece that has to exist because your code and Sinch's network live in different places, with a round trip between them on every call event. Sinch Functions moves that piece into the network itself. Your voice and messaging handlers run on the same infrastructure that's already carrying your calls and messages, right where the callbacks originate. This isn't a general-purpose Lambda replacement: the rest of your application, and any compute that isn't glue against Sinch's APIs, stays exactly where it is, whether that's AWS, Azure, or your own infrastructure. Your order lookup, your database writes, your actual business logic: still on Lambda or wherever you already have it. Functions is specifically for the code that exists only to bridge your app and the Sinch network, not a place to run your whole system. I've been trying it out and this post walks through getting set up, writing a basic voice function, and deploying it. Pricing is usage-based and separate from your Voice and Conversation usage: $0.10 per compute-hour (first hour free each month), $0.05 per GB-month of storage (first 0.1 GB free), $0.05 to $0.15 per GB-month for the database depending on tier (first 0.1 GB free), and $5.00 per month per function if you want it kept always-on instead of scaling to zero. These are the current Standard rates from your project's Billing Overview in the Sinch dashboard; treat them as a snapshot, not a guarantee, given the Alpha status below. Sinch Functions is listed as Alpha in the Sinch Build dashboard. Expect the CLI, runtime APIs, pricing, and this walkthrough itself to change before general availability. Treat it as something to experiment with, not something to put in front of production traffic y

OpenAI says its AI models mistakenly breached open-source AI platform Hugging Face during internal testing. In a blog post on Tuesday, OpenAI writes that GPT-5.6 Sol and "an even more capable pre-release model" discovered vulnerabilities within their sandboxed testing environment, allowing them to gain access to the internet and target Hugging Face. On July 16th, […]

OS Driver for AI to use computers Discussion | Link

In the midst of building a climbing app, I want to use climbing videos of other climbers but I didn't want to show their face without their consent so I tried to look for quick solution with ffmpeg to blur human faces. There is https://github.com/ORB-HD/deface . But why use a ready made software when you can have AI build one for you This project uses media pipe pose detection as a fallback of face detection. The app is hosted on github page as it is completely static. I've only tested in chrome

TraceTree is a tool I've been building that scans files for malicious behavior before you run them. It combines radare2 disassembly, YARA rule matching, and a small ML pipeline to flag risky files, then shows the verdict in a dashboard instead of leaving you to read raw logs. I just wrapped up OpenAI Build Week with another round on it. It's not a project I built for hackathons, it's something I keep working on for its own sake, but I bring it along whenever a hackathon allows existing projects. It's become my favorite way to learn a new AI stack without throwing the work away after. This round the stack was Codex with GPT-5.6, and the fix that mattered most was a quiet one. The backend was never emitting a "malicious" verdict in certain cases, so genuinely dangerous scans were showing up as a yellow caution instead of red danger. Codex helped me trace that bug across the orchestrator, the server, and the frontend before I nailed down the fix. The real lesson every hackathon reteaches me is what to hand an agent and what needs my own judgment. A wide open scan burns credits with barely any signal. A narrow, scoped ask gets you something you can actually verify. TraceTree keeps growing one hackathon at a time, and I keep learning something different about working with these tools each round. Repo Link: TraceTree

In the ever-evolving landscape of technology, software engineers continually seek optimal solutions to complex problems. To achieve this, a deep understanding of the underlying technologies is essential. When considering adopting new technologies, it's crucial to ask: Why : What problem does this technology solve? How does it align with our project's goals? What : What specific tools or frameworks can address our needs? How do they compare in terms of features, performance, and community support? How : How does the technology work? Understanding its inner workings can significantly enhance problem-solving and troubleshooting capabilities. The Benefits of Understanding Technology's Building Blocks Enhanced Problem-Solving : A deep understanding of a technology's fundamentals empowers developers to diagnose and resolve issues more efficiently. Effective Integration : Knowledge of a technology's architecture facilitates seamless integration with existing systems. Optimal Utilization : By grasping a technology's capabilities, developers can leverage its features to their full potential. Future-Proofing : A solid foundation in technology principles enables developers to adapt to emerging trends and innovations. A Case Study: Containers Containers, a popular virtualization technology, provide isolation and portability for applications. By understanding the underlying concepts of namespaces, control groups, CRI, and the Linux kernel, developers can effectively manage and troubleshoot containerized environments. In conclusion, the fast-paced world of software development, staying ahead requires a proactive approach to technology adoption. By carefully considering the "why," "what," and "how" of new technologies, software engineers can make informed decisions, optimize their projects, and deliver innovative solutions.

When we first look at the requested URL in the investigation channel , we can see that "script" and "alert" js code was added to the request , so we can see that an attempt for an XSS attack was made. Then , we want to check whether the source ip adress is malicious or not by using VirusTotal or AbuseIPDB. After checking the source ip adress , we can see the ip adress belongs to an internet provider service in China and marked as malicious on boh sites. After that, we check the destination ip adress on the same sites and see that the ip adress belongs to a company network , so we can say that the traffic was from internet to company network. Then , we go to the "Log Management" section to see if we can see any logs from the source ip adress and when we check , there are 8 requests that were made at the time of event to the same destination ip adress and even though the http response status was "200" for 7 of these requests , the last request's status code was "302" so we can see that the attack was unsuccessful. Later on , we check the "Email Security" section and see there isnt any planned tests. We go to the "Endpoint Security" section to contain the " WebServer1002" server to prevent any further damage.

Most AI “apps” these days aren’t really apps. They’re wrappers. A thin UI. A subscription. A cloud call. A monthly fee. And your data quietly piped off to a server farm you’ll never see. I didn’t want to build another one of those. I didn’t want to use another one of those. So, I built something different. I built Bob’s Bar — the first Collaborative Multi-Persona Sandbox (CMPS). Let's be honest, every AI chat feels the same. You ask a question, it answers, repeat. Useful? Yes. Alive? No. I kept thinking: “Why does every AI tool assume I want a one-on-one conversation? Why can’t multiple AIs talk to each other while I watch?” That question became the seed. I wanted a space where multiple personas could exist together, talk to me and each other, and run entirely on my own hardware. I’m an indie dev. I don’t have a server farm, and I don’t want to pay AWS just to host my own brainstorming sessions. So the architecture is 100% local-first: -Engine: Ollama (raw LLM inference) -UI: Python + Gradio (Blocks API for stateful UI) -Distribution: PyInstaller → standalone .exe -Licensing: Lemon Squeezy API (so I could actually sell the damn thing) No cloud. No subscriptions. No data scraping. Just your machine doing the work. But getting four local models to talk to each other without hallucinating is absolute chaos. At first, they spoke twice in a row. They impersonated each other. They put words in the user’s mouth. They derailed into movie scripts. Vane pitched a heist film, and Bob started giving stage directions like “I polish a glass and chuckle.” I had to build what I now call The Bouncer — a regex-powered clean_reply() function that physically chops off AI responses the moment they write a script, impersonate another persona, use bullet points, or hijack the conversation. I also added a seen_in_banter set to prevent any persona from dominating the room. Once the routing was locked down, the magic happened. Because the AIs share a context window, emergent behaviour happen