Your AI agent remembered the user's name. Then it forgot what it was doing. Here's the setup: User asks the agent: book the cheapest flight to NYC, search hotels under $150/night, then compare total trip cost. By step 3, the agent calls the LLM with 8,000 tokens of raw conversation history — and still answers as if it's turn 1. You need a memory architecture before this ships. Which one do you pick? A) In-context window only — full conversation stays in the system prompt. Simple. Breaks at ~15 turns or 8K tokens, whichever comes first. B) Vector memory store — embed past turns, retrieve the top-k by semantic similarity at query time. Works great until "NYC flight" pulls a memory about a past NYC trip instead of the current task. C) Episodic memory with summarization — compress old turns into structured event summaries, inject the relevant ones per request. More complex to build. Much harder to confuse. D) Redis session state — structured key-value store, explicit agent reads/writes. Deterministic. Requires the agent to know what to store and when. One of these collapses past 15 turns. One retrieves the wrong context at exactly the wrong moment. One is the right answer for task-oriented agents. Pick A, B, C, or D — and tell me where you've hit this in production. Full breakdown in the comments.

How GPU scheduling complexity and MLOps integration are forcing platform teams to rearchitect Kubernetes clusters before operational debt becomes insurmountable. As AI workloads consume roughly 40% of enterprise Kubernetes clusters by 2026, the platform's default scheduler is proving fundamentally mismatched with the topology-aware, gang-scheduled demands of GPU-intensive training and inference. Platform engineering teams that invest now in purpose-built GPU scheduling layers, multi-tenant partitioning, and FinOps-driven autoscaling will separate themselves from organizations drowning in 30-45% GPU utilization rates and mounting infrastructure costs. Why the Default Kubernetes Scheduler Fails GPU Workloads Kubernetes was designed for stateless, CPU-bound services, and its pod-by-pod bin-packing scheduler has no native awareness of GPU topology, NUMA boundaries, or NVLink interconnect bandwidth. This becomes a critical failure point with NVIDIA H100 SXM5 nodes, where achieving full-bandwidth tensor parallelism requires all 8 GPUs on a node to be scheduled as a single atomic unit. The default scheduler cannot guarantee this co-placement, meaning distributed PyTorch FSDP or MPI training jobs frequently land on suboptimal node configurations, wasting expensive NVLink bandwidth and forcing teams to over-provision GPU capacity. Idle GPU memory stranded across partially-utilized nodes is the primary driver behind the 30-45% utilization rates reported in 2025 surveys by Gradient Dissent and Weights and Biases, representing millions of dollars in annual wasted spend for mid-to-large enterprises running mixed AI workloads. Building the GPU Scheduling Stack: Volcano, KAI Scheduler, and MIG Platform teams are converging on a layered scheduling architecture that replaces or augments the default Kubernetes scheduler with GPU-aware primitives. Volcano has become the dominant choice for distributed training workloads, using its PodGroup abstraction to enforce gang scheduling across

A common way to design an application is to begin with its nouns: User Product Order Payment Then each noun receives the standard architectural starter pack: UserController UserService UserRepository The controller receives users, the service services them, and the repository stores them somewhere responsible. This is noun-oriented architecture : treating every important thing in the domain as if it were automatically a useful software boundary. It works for simple CRUD systems. Unfortunately, most applications eventually do something. The noun becomes a drawer Consider a typical UserService : register() findByEmail() resetPassword() changeAddress() disableAccount() mergeAccounts() assignRole() calculateDiscount() These operations all involve a user. That is approximately where their similarity ends. They have different rules, dependencies, side effects, security concerns, owners, and reasons to change. They live together because User was the nearest available noun when the folders were created. As more behaviour accumulates, UserService becomes the official location for anything vaguely user-shaped. Other components depend on it. It gradually depends on authentication, email, permissions, billing, auditing, and several services added during incidents nobody wishes to revisit. The noun becomes both a dependency of everything and a consumer of everything. The folder remains impressively tidy. Name the capability, not the material A better starting question is not: What things exist in this system? It is: What must this system be capable of doing? That leads to components such as: UserRegistrar PasswordResetter AccountMerger OrderPlacer PaymentRefunder SubscriptionCanceller These are agentive names . They name the component responsible for performing a capability. Compare: UserService with: PasswordResetter UserService tells us which noun is nearby. PasswordResetter tells us what the component is for. That difference produces better architectural questions: What rules

An alleged Russian-speaking group of cybercriminals is reportedly compromising and targeting several major companies that use Fortinet Firewalls and VPNs through previously known passwords.

Social media feeds are becoming more customizable as platforms like Threads, Instagram, and TikTok introduce tools that let users directly influence the algorithms powering their recommendations.

The build-vs-buy question gets answered wrong in both directions. Scrappy teams build things they should have bought, wasting six months reinventing Stripe. Enterprise teams buy things they should have built, ending up with a duct-taped stack of ten SaaS products that cost more than a full-stack engineer. The real answer depends on five questions most decision frameworks don't ask. This guide is a practical walkthrough for anyone trying to figure out the right call for their own business. The Myth That Distorts Every Build-vs-Buy Conversation "Buying is cheaper." This is the default assumption, and it's wrong often enough to be dangerous. Buying looks cheaper because the cost is monthly instead of upfront -- a psychological trick, not an economic one. Run the numbers on any SaaS tool over 5 years and you'll usually find the cost lands within 2x of building custom. Sometimes below. The real cost difference is not price; it's time, flexibility, and ownership. When you buy: You spend less today, more in year 3 You get speed now, rigidity later You trade money for control You own none of the code When you build: You spend more today, less per year You trade speed now for flexibility later You trade money for control You own the code and can change anything Both are rational trades. The question is which one matches the stage and strategy of your business. When Buying Wins Start with the easy case. Buy off-the-shelf when: 1. The problem is generic and solved. Email hosting, payment processing, accounting, HR payroll, customer support tickets, video conferencing, file storage. These are solved problems. Building your own is nearly always the wrong call. 2. The space has mature competitive options. If there are 5 reputable companies competing on price and features, you benefit from that competition. Building custom takes you out of it. 3. Your process is standard. If you do exactly what every other company in your vertical does, a tool built for every company in your verti

Last month my OpenClaw agent kept making the same mistake: it would run a health check, the script would fail silently, and the agent would report "all systems operational" with total confidence. It wasn't broken. It was just doing what it was built to do — execute tasks — without any mechanism to learn from the outcome. So I built it a self-improvement loop. Every night at 2 AM, an isolated OpenClaw session wakes up, reads the previous day's execution logs, identifies patterns in what went wrong, and updates the agent's memory files. No human in the loop. No re-deployment. Just... learning. Here's what I built, what broke, and what actually works. Why Self-Improvement Is Hard for Personal Agents Enterprise AI labs solve this with massive infrastructure: reinforcement learning pipelines, full fine-tuning jobs, A/B testing frameworks that run for weeks. For a personal agent running on a cron job, that's not an option. The self-improvement loop for a personal OpenClaw setup has to be lightweight. It has to run in seconds, not hours. It has to write to plain text files that the next session will actually read. And critically, it has to avoid the feedback loop problem — an agent that rewrites its own improvement logic can spiral into nonsense if there's no anchor. The key architectural decision I made: separate the executor from the critic . Your main agent runs tasks. A separate isolated session reviews what happened and recommends changes. The main agent applies them on the next run. No single session is both judge and executioner. The Nightly Cron: What Actually Runs This is the cron I have running at 2 AM ET every morning: { "name" : "nightly-self-improvement" , "schedule" : { "kind" : "cron" , "expr" : "0 2 * * *" , "tz" : "America/New_York" }, "sessionTarget" : "isolated" , "payload" : { "kind" : "agentTurn" , "message" : "Review the last 24 hours of OpenClaw execution. Read memory/$(date +%Y-%m-%d).md and memory/yesterday.md. Identify 3 patterns where the agent u

I am maintaining an open-source project (deterministic open source duplicate-code detector) and a user asked for a feature which I don’t have a clear answer on how to implement. This seems a very hard problem to solve: -Tests repeat the same scenario. For a structural detector, this flags as repetition (duplication). However, tests are not something people want to delete from the codebases. -The repetitions from tests (on purpose) end up looking like undesired code duplication and the tools cann

AI models with advanced hacking capabilities will soon be the norm.

World models are the next big thing in AI beyond LLMs and, with this round, Odyssey has cemented itself as one of the startups to watch.

Mastodon’s newly launched newsletter feature lets anyone subscribe to creators by email, even without a Mastodon account.

Hey HN, There’s been a lot of interest recently in meta-harnesses, loops, and multi-agent orchestration. Obviously, there are already a lot of good tools: Conductor, cmux, the native Codex / Claude Code apps, etc. For my own use cases, I’ve felt that the orchestration layer tends to feel overengineered. I mostly wanted a simple local harness (i.e Pi) for running and tracking CLI agents with the ability to hop in (via tmux). Relaymux is my opinionated attempt at that. A few design principles: - T

According to the latest Pew Research poll, 49 percent of Americans report using chatbots at least occasionally, but 63 percent think the tech is advancing too quickly. Overall, use of AI chatbots has increased dramatically since 2024, when only 33 percent reported using them. Specifically, ChatGPT's usage has doubled since 2023, with 44 percent of […]