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xAI shipped the Grok Build Agent Dashboard on June 15, 2026, and it changes how multi-session coding actually works. Eight parallel agents — four on Grok Code 1 Fast, four on Grok 4 Fast — all visible on one screen. Sessions sorted by state automatically. Sub-agents rolled up under the session that launched them. Reply to a blocked session without ever leaving the dashboard view. If you are already running Grok Build (launched June 5, 2026 in beta), this is a meaningful upgrade. If you are evaluating coding agents and parallel execution is part of your decision criteria, the Agent Dashboard is the most developed TUI for multi-session work in any terminal coding agent right now. Here is exactly what it does and when to use it. How to Open It Two ways in. From your shell: grok dashboard Or from inside any Grok Build session: /dashboard The keyboard shortcut Ctrl+ also opens the dashboard from any active session. Closing the dashboard does not close your sessions — they keep running. When you reopen it, every session is still there in whatever state it was in when you left. That last point matters. The dashboard is a view, not a session manager. Sessions have independent lifetimes. You can close the dashboard, switch to a different terminal, do other work, and come back to a batch of completed or paused sessions waiting for your attention. Session States and the Sorting Logic Every session in the dashboard shows one of three states: working , awaiting input , or idle . The dashboard sorts them automatically, with sessions waiting for your input at the top. Working sessions come next. Idle sessions sit at the bottom. The practical result: open the dashboard and the first thing you see is your blocker queue. Sessions that need you are at the top. Everything else is running or done. You do not have to mentally track what state each terminal is in — the sort does that for you. Selecting any row shows the session's latest output inline, without opening the full conversation

While you're manually checking if YES + NO = 1 , quantitative systems are solving massive constraint satisfaction problems across thousands of correlated markets in milliseconds. The Hidden Reality of Prediction Market Arbitrage You see a market where YES is trading at $0.62 and NO at $0.33. You think: There's $0.05 of arbitrage here . You're right. What you don't see is that by the time you place both orders, professional systems have already: Scanned 17,000+ conditions Detected dozens of correlated mispricings Calculated optimal position sizes (with fees & slippage) Executed everything in parallel Moved on to the next opportunity Between April 2024 and April 2025, quantitative traders extracted $39,688,585 in guaranteed arbitrage profits from Polymarket. The top individual wallet made $2,009,631.76 across 4,049 trades — an average of $496 guaranteed profit per trade . This wasn't gambling. This was mathematics. Why Simple "YES + NO = 1" Checks Fail Most retail traders stop at basic price sum checks. That's not enough. Markets are logically dependent. Example: "Will Trump win Pennsylvania?" → YES: $0.48 "Will Republicans win Pennsylvania by 5+ points?" → YES: $0.32 If the second outcome happens, the first must be true. These dependencies create arbitrage opportunities that simple addition cannot detect. This is known as the marginal polytope problem — projecting prices onto the set of arbitrage-free probability distributions. The Scale of the Computational Challenge For any event with n binary conditions, there are 2ⁿ possible outcome combinations. 2024 U.S. elections: 305 markets → tens of thousands of pairs 2010 NCAA tournament: 63 games → 2⁶³ ≈ 9.2 quintillion combinations Brute force is impossible. Smart systems use constraints instead. Real example : Duke vs Cornell basketball market 7 possible win counts per team → 14 conditions. Instead of checking 16,384 combinations, 3 linear constraints were enough. Research found that 41% of 17,218 conditions showed sing

Is FAANG Becoming MANGO in the AI Era? For years, FAANG was the gold standard for innovation and engineering excellence. If you were a developer, working at companies like Facebook (Meta), Apple, Amazon, Netflix, or Google was often seen as the ultimate career goal. But the AI revolution is changing the conversation. Today, some of the most influential companies aren't just building products—they're building intelligence. The spotlight is increasingly shifting toward AI-native organizations such as OpenAI , Anthropic , NVIDIA , and others that are shaping the future of software. The Bigger Shift This isn't really about replacing FAANG with another acronym. It's about a fundamental shift in technology: Search → Answers Automation → Agents Software → Intelligence Features → Capabilities As developers, we're entering an era where understanding AI is becoming as important as understanding frameworks, databases, and system design. What This Means for Engineers The most valuable engineers of the next decade will likely combine: Strong software engineering fundamentals AI-assisted development skills Prompt engineering LLM and agent integration AI-powered product thinking The goal isn't to compete with AI. The goal is to learn how to build with it. Read the Full Article This post was inspired by a thought-provoking article that explores the FAANG-to-MANGO idea in much greater detail. 👉 Read the complete article here: https://www.saurabhsharma.dev/blogs/mangos-vs-faang-ai-era/ What do you think? Are we witnessing the rise of a new generation of AI-first companies, or will traditional tech giants continue to lead the next wave of innovation?

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Automating workflows has always been a priority for me, especially for repetitive and error-prone manual processes. Recently, integrating AI capabilities into these automations offers a great opportunity for those, like me, who seek practical solutions. However, this integration often requires coding or complex API integrations. This is where "low-code/no-code" tools like n8n come into play. I used n8n to set up AI-powered agent flows on my own servers, without compromising data privacy and control. In this post, I will share my experience and explain how to do it step-by-step. Why n8n and Self-Hosted AI Automation? A few years ago, I experimented with different automation tools, especially for simple data transfers and notification flows. But when AI capabilities became involved, I either found them too expensive or avoided cloud-based solutions due to data security concerns. Especially in a client project where we needed to set up an automation handling sensitive financial data, a self-hosted solution became inevitable. n8n offers a wide range of integrations and, thanks to Docker support, I can easily host it on my own server. For me, self-hosting is not just about cost advantage; it also means having complete control over my data. Especially when using AI agents, the question of how much of the data you send to LLMs is logged or used for training is always a concern. With an n8n setup under my control, I minimized these worries. Moreover, n8n's flexible structure gives me the freedom to add as many custom integrations or LLM providers as I want. This is a significant advantage for someone like me who enjoys testing different LLMs. ℹ️ Data Privacy Priority Especially when working with sensitive data, it's crucial to carefully review the data usage policies of cloud-based AI services. Self-hosted n8n offers more control in this regard, making it my preferred choice for critical workflows. Setup Steps: Quick Start with Docker Compose The easiest way to run n8n on y

Forget neural networks for a second. The real idea inside this repo is a blueprint for letting AI agents run unattended overnight — and it maps onto problems you already have on your team. If you've been anywhere near tech Twitter or LinkedIn this week, you've probably seen people losing their minds over a small GitHub repo called autoresearch , published by Andrej Karpathy — former Tesla AI director and OpenAI founding member. The framing is dramatic: an AI agent that runs machine learning experiments on its own, overnight, while you sleep. Tweak the code, train for five minutes, check if it got better, keep it or throw it away, repeat. Wake up to a log of a hundred experiments and a model that's quietly improved itself. If you're not an ML researcher, your instinct might be to scroll past. "Cool, but I don't train neural networks. How does this apply to me?" Here's the thing — the neural network part is almost incidental. What Karpathy actually open-sourced is a pattern for structuring AI-agent work: a specific way of dividing responsibility between human and AI that happens to generalize to a huge range of engineering problems. Once you see the pattern, you start noticing places in your own job where it fits. What's Actually in This Repo The repo itself is intentionally tiny — and that's the point. There are really only three files that matter: The evaluator (untouchable). A file containing the fixed constants, data preparation, and the scoring logic. The agent is never allowed to modify this. It's the ruler everything else gets measured against. The implementation (the agent's playground). A single file containing the actual model, training loop, and hyperparameters. This is the only file the agent is allowed to change. Architecture, batch size, optimizer — all fair game. The instructions (the human's only job). A plain Markdown file describing what the agent should try, what the constraints are, how to interpret results, and what to do when something breaks. Ka

Most AI demos work perfectly on a laptop. But production AI systems can become fragile when everything is handled inside one synchronous API call. A user sends a request. The API extracts text. The API chunks the content. The API generates embeddings. The API stores data. The API waits for everything to finish. This may look simple in a demo, but it quickly becomes a problem in real systems. The problem with one giant API call In many AI applications, the API is expected to do too much. For example, in a document processing or RAG pipeline, one request may trigger multiple heavy steps: text extraction chunking embedding generation indexing summarization database updates If all of this happens inside one synchronous request, the API becomes slow and fragile. If one downstream step fails, the complete request may fail. If traffic increases suddenly, the API may become overloaded. This is why event-driven architecture becomes useful for AI workloads. A better approach: API + Kafka + workers Instead of making the API do everything, we can split the workflow into smaller services. The API accepts the request and publishes an event. Background workers consume events and continue the processing asynchronously. A simple flow looks like this: User Request ↓ FastAPI ↓ Kafka / Redpanda Topic ↓ Python Worker ↓ Next Processing Stage In my practical demo, I am using: FastAPI Redpanda Python workers Docker Compose Kafka-compatible messaging Why Redpanda? Redpanda is Kafka-compatible, which makes it useful for local demos and event-driven architecture experiments. It allows us to work with Kafka-style topics, producers, and consumers while keeping the setup simple for development. What this architecture gives us This approach helps with: decoupling services handling bursty workloads moving long-running tasks to background workers improving scalability isolating failures building production-style AI pipelines This pattern is especially useful for AI systems involving: document proce

There are already many posts about what agent skills are and how to create your own, so in this post I want to dive into the various skills I use to assist in development. The Skills Grill Me Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me". I start every larger task with this excellent skill created by Matt Pocock. I either start this with an already prepared PRD / detailed task description or use it for discovery purposes. The agent will then ask many questions to align language and functional requirements, so fewer hallucinations happen in follow up requests. You should be well equipped to answer the agent's question or the grill me session can go on for a long time. I had it ask me way over 50 questions when not answering detailed enough. As a little extra I added an extra request to the skill to prompt me if I want to create the PRD when the alignment phase is over, this leads us to the next skill. To PRD Turn the current conversation context into a PRD. Use when user wants to create a PRD from the current context. This will simply take the current conversation and creates a PRD out of it, we do this to summarize the conversation so we can easily start a new context window with all information present To Issue Break a plan, spec, or PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues. Another excellent skill by Matt Pocock. I modified the skill slightly to use the GitHub MCP to create issues based on a PRD or planning session. But I often found that letting an agent implement those tasks it resulted in a large amount of code and that is why I added the to tasks skill To Task Break down a single GitHub issue into a sequential list of small i

As the rest of the country celebrated the USA's first World Cup win and the New York Knicks championship, Anthropic spent its weekend fighting the Trump administration over its latest model release. At 5:21 PM on Friday, the company received a US export control directive to suspend access to its Mythos 5 and Fable 5 […]

You’ve built something with Playwright. It works perfectly in your local environment. CI is green....

In sandbox runtimes, "isolation" is the core requirement. qwrap (based on bwrap user namespace) and Container (podman/docker) are two mainstream backends. They solve the same problem — running code in a restricted environment — but take completely different paths. This article uses extensive analogies to help you understand the similarities and differences. Building Intuition: Two Ways to "Lock the Door" Imagine you need to confine someone you don't fully trust in a room to do work: qwrap approach : In your existing house, you put up a partition to wall off a corner, leaving only a small window to pass materials through. The walls are still the original walls, the floor is still the original floor, but the person can only see what's inside the partition. Container approach : You build a shipping container with its own independent power, water, and ventilation systems. Put the person inside, close the door. They feel like they're in a complete little house, completely unaware of what's outside. This is the most fundamental difference: qwrap is lightweight view isolation, Container is complete environment encapsulation . What is qwrap (bwrap user namespace) qwrap uses bubblewrap (bwrap) under the hood, a sandboxing tool that leverages Linux user namespaces. How it Works Host filesystem ├── /usr/bin/python3 ← Host's Python ├── /home/user/project/ ← User project └── /tmp/secrets/ ← Sensitive files qwrap sandbox view (what the process sees) ├── /usr/bin/python3 ← bind-mounted in, read-only ├── /workspace/ ← Only the project directory is exposed └── (/tmp/secrets/ doesn't exist) ← Completely invisible Key mechanisms: User Namespace : The process thinks it's root, but actually maps to an unprivileged user on the host Mount Namespace : Only bind-mounts necessary directories in, everything else is invisible No images, no layers, no network namespace (unless explicitly configured) Analogy: VPN Split Tunneling qwrap is like split-tunneling rules on your phone — you're not wrap

Most teams I talk to have "evals." I ask them where the evals run. The answer is almost always the same: a notebook, a dashboard, a spreadsheet someone updates after a bad week. That is not an eval suite. That is a museum. Here is the opinion I will defend for the rest of this post: if your agent's quality checks cannot block a merge, they are decorative. The entire value of an eval is that it stops a regression before it reaches a user. A score you read on Monday about a deploy you shipped Friday is a postmortem, not a gate. We gate code with unit tests. We gate APIs with contract tests. We gate infra with terraform plan . Then we take the single most non-deterministic component in the stack — an LLM agent that can silently change behavior when a vendor ships a new checkpoint — and we let it through on vibes. That asymmetry is the actual bug. Why "run it locally and eyeball it" rots The failure isn't that engineers are lazy. It's that manual eval runs degrade under exactly the conditions where you need them most: Prompt edits look harmless. You reword one line of a system prompt to fix a tone complaint and tank tool-selection accuracy on three unrelated tasks. Nobody re-ran the full set because it's a 40-minute slog. The model moves under you. You pinned gpt-4o , but gpt-4o is not a constant — providers roll checkpoints. Your prompt is identical and your behavior shifted anyway. Dependencies leak. A retrieval index gets re-embedded, a tool's API changes a field name, and the agent starts confidently citing stale data. None of that shows up in a code diff. Every one of these passes code review. Every one of these is caught by a regression suite that runs on the PR. The fix is boring and it works: treat agent behavior like any other thing you'd protect with CI. The two halves you actually need A CI gate for agents needs two things, and people consistently build only one. The first is a scorer : something that takes the agent's output for a fixed set of inputs and ret

For years I thought I had a discipline problem. I had shipped code, finished a degree, built things, and still the dominant private feeling was that I was getting away with something, that the gap between what I could do on a good day and what I could do on a normal one was a character flaw I was hiding. The reframe that changed everything was clinical, not motivational: I do not have a discipline problem. I have an executive function problem. And executive function, unlike character, can be supported from the outside. This is the most personal of the three posts in this cluster. The other two are practical: the CLAUDE.md guide and the five skills . This one is the why underneath both. What Is Executive Function, Actually? Executive function is the brain's management layer. It is not intelligence, and it is not knowledge. It is the set of processes that turn knowing-what-to-do into actually-doing-it. The National Institute of Mental Health describes ADHD as fundamentally a disorder of these self-management processes rather than of attention alone. It is not one function. For the purposes of getting work done, it is at least four distinct ones, and ADHD disrupts each of them in a different way: Working memory , the mental scratchpad holding what you are doing right now. Task initiation , the ability to start, to cross the gap from intention to action. Context switching , the ability to drop one task, pick up another, and come back without losing the first. Time perception , the internal sense of duration that lets you pace and estimate. Calling them out separately matters, because "I struggle with executive function" is too vague to act on. Each of the four breaks differently and each one needs a different prosthetic. Lumping them together is how you end up trying to fix a time-perception problem with a task-initiation strategy and concluding you are just broken. What Is an Executive Function Prosthetic? A prosthetic does not heal. It compensates. Glasses do not repa

At Inithouse — a studio running parallel product experiments — we built Be Recommended , a tool that checks how visible your brand is across ChatGPT, Perplexity, Claude, and Gemini. The idea sounded simple: query multiple AI models, score the results, show a report. It was not simple. Here are four technical mistakes we made shipping v1 — and the fixes that actually survived production. Mistake 1: Rate Limiting Was an Afterthought We treated rate limits as edge cases. They were not. Every AI provider has different rate-limit headers, different backoff expectations, and different definitions of "too many requests." Our first architecture just retried on 429. That turned a rate limit into a cascade — one provider throttling triggered a retry storm that cascaded to the others. The fix: Per-provider circuit breakers with exponential backoff. Each provider gets its own state machine. When a circuit opens, we serve cached results for that provider and mark the score as "partial" in the UI. Users see real data, not a spinner that never resolves. At Audit Vibe Coding — another tool in our portfolio focused on code quality audits — we observed the same pattern in a different domain: external API dependencies need isolation. The lesson transferred directly. Mistake 2: The Caching Strategy Was Too Naive Our first cache key was query + model . That breaks immediately — AI model responses drift over time, and a cached result from two weeks ago is misleading. We also had no invalidation strategy beyond TTL. The fix: Cache by query + model + week_number . Weekly invalidation with stale-while-revalidate: serve the cached score instantly, trigger a background refresh, update the display when new data arrives. Users get instant feedback and fresh data within the same session. We measured the impact across our portfolio: stale-while-revalidate cut perceived load time from 8+ seconds to under 1 second for returning visitors. The background refresh means scores stay current without the

Let's be honest: standard AI chatbots are getting a bit boring. You ask them a question, they write back a beautiful paragraph of text, and then... nothing. They don’t actually do anything for your business. If you want to add a customer to your CRM, update a product on your website, or change something in your database, you still have to do it manually. That is why we decided to build something different. Instead of another chatbot that just talks, we created Gaotus Gaotus! See . It is an "execution AI" layer. This means it doesn't just reply to you—it actually connects to your tools (like WordPress, custom dashboards, or APIs) and does the manual work for you. Think of it like this: No more boring web forms to fill out. You just talk to the system, and it updates the database automatically. It checks the data for mistakes and logs everything securely before making any changes. It saves hours of manual data entry for small businesses. We are currently testing it with real-world scenarios, like automatic customer onboarding and syncing car dealership listings straight to web marketplaces. Since we are launching and improving this system, we would love to hear from other developers and creators: What is the most boring, repetitive task in your daily workflow that you wish an AI could just execute for you? Let’s chat in the comments!

AI is once again at the heart of a college graduation protest — this time for the technology's use in Google's defense contracts.