Dev Systems
Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale
We’re sharing insights into Meta’s Capacity Efficiency Program, where we’ve built an AI agent platform that helps automate finding and fixing performance issues throughout our infrastructure. By leveraging encoded domain expertise across a unified, standardized tool interface these agents help save power and free up engineers’ time away from addressing performance issues to innovating on new products.We’ve built a unified AI agent platform that encodes the domain ex
Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways
We’re sharing lessons learned from Meta’s post-quantum cryptography (PQC) migration to help other organizations strengthen their resilience as industry transitions to post-quantum cryptography standards.We’re proposing the idea of PQC Migration Levels to help teams within organizations manage the complexity of PQC migration for their various use cases.By outlining Meta’s approach to this work — from risk assessment and inventory through deployment and guardrails — we hope to contribute practical
Show HN: Agentfab – A Distributed Agentic Platform
Hi HN,I’m the creator of agentfab, a distributed agentic platform that features task decomposition, multi-agent orchestration, model heterogeneity with custom agentic fabrics, bounded review loops, and a bespoke self-curating memory system that enable shared context.My background is in engineering at hyperscalers where I worked extensively with foundational distributed systems. I started agentfab because I wanted an agentic coding tool that could effectively decompose and parallelize work across
Ask HN: How to test a distributed Job Runner with an at-most once execution
Hey everyone.
I'm currently building a distributed job runner that can guarantee an at-most once execution under crashes & system failures.I'm still in the early stages, building it from scratch.
Think of it as Sidekiq, but with at-most-once execution guaranteeHas anyone done something related to this for a side project and how did they test & validate it.
Also, has anyone built something like this for work to be used in Production? any ideas you can share?What i plan to do for
What if Time at the subatomic level has multiple arrows?
I’ve been wondering about one thing: what if the electron isn't "blurred" in space, but simply distributed across an infinite number of time-arrows simultaneously?
Is it possible that quantum superposition is merely an observational effect of viewing such a multi-vector system from the perspective of our single timeline?
And if so, wouldn't superconductivity become a problem of temporal synchronization rather than thermodynamics?
— MultiLineArtist
Ask HN: Sanity-check my numbers on EVs and solar power
I've been thinking a lot lately about solar power, and specifically about the supposed need for expensive and heavy steel structures to hold panels in place, at least in some applications. And I've also been thinking about electric vehicles, and about how electric engines are much more efficient than internal combustion engines, even before considering whether the source of energy is renewable.And the idea occurred to me: why not just put solar paneling on the roof of the vehicle itsel
Databricks Delta Sharing: Enabling Cost Efficient Cross Cloud Data Access
What Is Delta Sharing?Delta sharing is an open protocol for secure data sharing that allows organizations to share live data stored in Delta Lake with external consumers—across cloud providers such as Azure, AWS, and Google Cloud—while keeping the data in its original location. Consumers access the same up‑to‑date data without the provider having to copy or move it to another cloud. Why Delta Sharing Matters in Multi‑Cloud ArchitecturesIn multi‑cloud environments, data sharing typically lea
Building a Scalable IoT Platform for Facility Management with Azure Serverless Services
The platform follows a microservices architecture with six independently deployable services, all built on Azure Functions v4 with TypeScript. Each service is containerized and deployed to Azure Container Apps.┌──────────────────────────────────────────────────────────────────────────────┐ │ Enterprise IoT Platform │ ├──────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────────────┐ │ │ │ IoT Portal U
Show HN: Superpowers-UML – UML-Enabled Superpowers
Superpowers-UML modifies Superpowers to ensure a software development workflow in which AI agents design through UML modeling, for Claude Code users.
Show HN: I built a Cargo-like build tool for C/C++
I love C and C++, but setting up projects can sometimes be a pain.Every time I wanted to start something new I'd spend the first hour writing CMakeLists.txt, figuring out find_package, copying boilerplate from my last project, and googling why my library isn't linking. By the time the project was actually set up I'd lost all momentum.So, I built Craft - a lightweight build and workflow tool for C and C++. Instead of writing CMake, your project configuration goes in a simple craft.
Show HN: Skilldeck – Desktop app to manage AI agent skill files across tools
Skill files (.claude/skills/, .cursor/rules/*.mdc,
AGENTS.md, .windsurfrules) are becoming a core part of
AI-assisted development workflows. The problem: they
scatter across projects, diverge silently, and every new
repo means rebuilding behavioral config from scratch. Each
tool uses a different format and location.Skilldeck keeps one local library and deploys to any tool
in the correct format automatically. Ten built-in target
profiles cover Claude Code, Cursor, Copil
Show HN: Recursive-Mode for Coding Agents
recursive-mode is an installable skill package for coding agents. It gives your agent a file-backed workflow for requirements, planning, implementation, testing, review, closeout, and memory, instead of leaving the whole process scattered in context.Long-running agent work has a common failure mode: requirements, decisions, and plans live in the conversation. Once the session ends or the context window overflows, the agent loses track of what was decided, what was implemented, and why.recursive-
Enterprise AI does not have a model problem. It has an adoption problem
Enterprise AI does not have a model problem. It has an adoption problem.Today, Fortune highlighted the gap clearly: Companies are pouring tens of millions into AI, while 80% the white collar workforce are either bypassing the tools, not using them, or just straight up sabotaging them. That is not because employees are lazy. It is because most companies rolled out the Ferrari before giving their white collar employees the reason to want to drive the Ferrari, instead of their regular Toyota Prius.
Show HN: Django-security-hunter – Django security scanner CLI
I built a lightweight CLI tool for Django and Django REST Framework projects to help detect common security issues early in development.It focuses on problems like:unsafe production settings
exposed or misconfigured APIs
missing security configurations
common security risks in Django projectsThe goal is to make security checks simple and part of everyday development or CI workflows.GitHub: https://github.com/abu-rayhan-alif/djangoSecurityHunter
UK Department for Transport accelerates public policy insights with Google Cloud AI
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/UKTransport-hero_v2.max-600x600.format-webp.webp">The UK’s Department for Transport uses Google Cloud AI to analyze feedback.
Show HN: 500k+ events/sec transformations for ClickHouse ingestion
Hi HN! We are Ashish and Armend, founders of GlassFlow.Over the last year, we worked with teams running high-throughput pipelines into self-hosted ClickHouse. Mostly for observability and real-time analytics.A question that came repeatedly was:
What happens when throughput grows?Usually, things work fine at 10k events/sec, but we started seeing backpressure and errors at >100k.When the throughput per pipeline stops scaling, then adding more CPU/memory doesn’t help because often part
Show HN: Collabmem – a memory system for long-term collaboration with AI
Hello HN! I built collabmem, a simple memory system for long-term collaboration between humans and AI assistants. And it's easy to install, just ask Claude Code: Install the long-term collaboration memory system by cloning https://github.com/visionscaper/collabmem to a temporary location and following the instructions in it.
To collaborate with AI over weeks, months, or even years, there needs to be a shared conceptual understanding of:- History (episodic memory): wh
Show HN: Build queryable packs for AI agents from videos, podcasts, and files
Hi,This started from a pretty personal use case.There was this very technical person I follow who would go live on YouTube from time to time. He has a ton of experience, and would casually drop really good insights about software architecture, engineering tradeoffs, and just general "you only learn this after years" kind of stuff. He also posts shorter clips, but I wanted something else: I wanted that knowledge to be always there, queryable whenever I needed it.At the same time, I was
Show HN: OS Megakernel that match M5 Max Tok/w at 2x the Throughput on RTX 3090
Hey there, we fused all 24 layers of Qwen3.5-0.8B (a hybrid DeltaNet + Attention model) into a single CUDA kernel launch and made it open-source for everyone to try it.On an RTX 3090 power-limited to 220W:
- 411 tok/s vs 229 tok/s on M5 Max (1.8x)
- 1.87 tok/J, beating M5 Max efficiency
- 1.55x faster decode than llama.cpp on the same GPU
- 3.4x faster prefillThe RTX 3090 launched in 2020. Everyone calls it power-hungry. It isn't, the software is.
The conventional wisdom NVID
Mythos, Glasswing, and the hardware disclosure problem nobody is discussing
Coverage of Anthropic's Claude Mythos Preview and Project Glasswing has focused almost entirely on software vulnerabilities. That is where the demos are and where controlled release maps cleanly onto existing disclosure practice. I have not seen anyone engage with the next obvious question: what happens when a Mythos-class model is given detailed hardware architecture documentation and asked to do a security-oriented review? My intuition is the hardware case is meaningfully worse, for reaso