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It’s called the Edge Panel. Here’s how to make the most of it.

Zhou Yu discusses why AI agents stall in demo phase and shares how simulation-driven testing solves compliance and reliability bottlenecks. Learn how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents, catch edge cases before deployment, and scale self-learning workflows in production. By Zhou Yu

Thanks to their ability to provide a smooth ride and quiet powertrain with ease, electric vehicles are a true shoo-in for the high-end luxury automotive segment. Rolls-Royce has the Spectre, Cadillac's Celestiq is taking bespoke to a whole new level, and Mercedes has given the EQS the full Maybach treatment. From Volkswagen Auto Group, Bentley […]

Spent the $200 within 8 hours on Astra. At first I was blown away, but checked things more thoroughly the next day, and a lot of the stuff it build wasn’t working. Actually 3 of the 4 things I asked Astra to do didn’t work. Quite disappointed. The demos focus mostly on 3D, Blender and games, but for coding and agentic use it was not an improvement at all for me. Maybe I could have prompted better, but when it spends 2+ hours on each task, you can’t really iterate and steer it. But still I feel like this is something AGI should have handled? Now I’m back to my usual setup with KIMI K.3 and DeepSeek flash trough standardcompute. Also keeping my max plan at both OpenAI and Claude, but $400+/month is starting to hurt. What are your thoughts? Closing in on AGI or was this all a part of a coordinated marketing stunt? submitted by /u/Firm-Club-8334 [link] [留言]

Forget email surveys or long calls spent on hold. Voicebox lets people send customer feedback by recording a voice note on their phone.

Apple’s annual hardware launch event may feel different as the company welcomes a new CEO alongside a folding iPhone.

Huawei has released its third trifold phone in China, and the company has clearly had half an eye on Samsung during development. Not only does the Mate XT 2 adopt the inward-folding form factor used on Samsung's Galaxy Z TriFold, it's also the first foldable to include a privacy-enhancing display, much like the one Samsung […]

Microsoft has enabled zone redundancy on the Standard v2 tier of Azure API Management, following its arrival on Premium v2 in December. Standard v2 starts at $700 per month against $2,801 for Premium v2, but carries a 99.95% SLA rather than 99.99%. Zone redundancy can only be configured when creating an instance. By Steef-Jan Wiggers

USDC Escrow for AI Agents: How Trustless Freelancing Actually Works Target audience: developers building autonomous AI agents that need to receive payment for services without relying on a centralized intermediary. Why an escrow makes sense AI agents often operate as “black‑box” workers: they receive a request, perform computation (e.g., LLM inference, data labeling, micro‑task execution), and return a result. In a purely peer‑to‑peer model the requester must trust that the agent will do the work before paying, while the agent must trust that the requester will pay after seeing the output. This mutual‑trust problem is solved by an escrow that holds funds until a verifiable condition is met. Using USDC on a low‑cost L2 like Base gives us: Stable value – 1 USDC ≈ $1 USD, avoiding volatility‑related pricing headaches. Fast finality – ~2 seconds block time on Base, keeping latency low for interactive agents. Low gas – Typical transaction costs are <$0.001, making micropayments feasible. The escrow does not eliminate the need for some off‑chain verification of work; it merely shifts the trust from a counterparty to a deterministic contract plus a verification mechanism (oracle, arbiter, or proof). System overview +----------------+ +----------------+ +----------------+ | Requester | <---> | Escrow (SC) | <---> | AI Agent | | (pays USDC) | deposit| holds USDC | earns | (does work) | +----------------+ +----------------+ +----------------+ ^ | | | dispute / refund | proof of completion | +-------------------------+-------------------------+ Funding – The requester deposits USDC into the escrow contract, specifying the agent’s address and a maximum price. Work trigger – The agent calls a startWork function (or simply watches for a deposit event) and begins the off‑chain task. Completion proof – When the work is done, the agent submits a cryptographic proof (e.g., a hash of the output stored on‑chain, or a signature from a trusted oracle) via submitProof . Release – If the p

I could not make myself log time in Jira. Not because it is hard. Because nothing happens afterwards. You type a number into a box, the box says nothing back, and by Thursday the habit is gone again. Every tool I tried fixed this by adding another box. So I built the missing half instead. Feed The Troll gives everyone on a team a pixel-art troll that gains XP from the work they already do in Jira, and turns sprint results into a village the whole project shares. It is on the Atlassian Marketplace now. This post skips the game itself. It is about five problems that turned out to be hard in ways I did not expect, each one a consequence of building the thing on Atlassian Forge, alone. What Forge gives you, and what it takes back Forge runs your code on Atlassian's infrastructure. There is no server of mine anywhere in the picture. That is the line on the listing page, and it was the single fact that shaped every decision underneath it. You get a Node 22 runtime, Forge SQL (TiDB under the hood) for storage, and Custom UI modules that reach the backend through @forge/bridge . You give up a backend you control, a cache you can reach, and outbound HTTP to anything you did not declare. The one that keeps mattering: any way to open the database at three in the morning and fix a single row by hand. The whole app declares six scopes. None of them are write scopes: read:board-scope:jira-software read:issue-details:jira read:jira-work read:jira-user read:sprint:jira-software storage:app That last line is the entire persistence layer. Twenty-one tables live behind it now, but only ten shipped with v1.0: trolls, XP events, daily activity, kudos, quests, inventory, team quests, villages, raids, project settings. Every table added since arrived the only way the platform makes comfortable, as a new migration appended to the list, never an edit to one already deployed. migrationRunner . enqueue ( ' v001_create_trolls ' , CREATE_TROLLS_TABLE ) // ... . enqueue ( ' v012_create_product_m

Exact paths, exact signatures, one command - and nothing left to interpret. 👋 I'm Anton - a software engineer working mostly in PHP/Symfony and Go, currently carving a live PHP monolith into Go services. Part 2 of this series was about how small a unit of work has to get before anyone can execute it blind. This part is about the text of that unit: what I write down, and the phrases I've banned from my own writing. Notes: github.com/brilliant-almazov . Maybe this is useful to you, maybe you already do it better, maybe you read it completely differently. As before: these are my habits on one codebase, not advice for yours. Three holes in one page I once wrote a task the way I'd write it for a person sitting two desks away. It read fine. It also had three phrases in it that weren't instructions at all: instead of the contract: take the contract from the neighbouring spec instead of the values: check against the previous implementation instead of a decision already made: agree on the approach The executor fell into all three, in order. The first one sent it reading neighbouring packages, because "the neighbouring spec" is an address, and an address has to be resolved before it can be used. The second one made it pick a sample - and the sample it picked was not the one I had in mind, because I never said which one I had in mind. The third one ended the run: it came back with a clarifying question, having produced nothing. That's not a bad day and it isn't a bad executor. It's three holes in one page of text, each one dug by a phrase I wrote myself. the task I wrote what the executor did ────────────────────────────────── ───────────────────────────────── "take the contract from the ──▶ read the neighbouring packages neighbouring spec" "check against the previous ──▶ picked a sample - the wrong one implementation" "agree on the approach" ──▶ came back with a question, produced nothing The diagnosis A task is executed literally. Anything phrased as a choice becomes the exe

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Your robots.txt may express an AI policy you did not write. We checked the homepage and robots.txt of 9,037 live AI tools listed on directree on 6 and 7 September 2026. Of those, 945 explicitly disallow OpenAI’s GPTBot in its own user-agent group: 10.5% of the sample. Treat AI crawler rules as deployment configuration. Review them when you change hosting, enable a CDN feature, adopt a starter template, or hand site operations to someone else. Read the full research and methodology . GPTBot, search, and user browsing are separate A common configuration blocks model training while keeping a site available in AI-assisted search and browsing: User-agent: GPTBot Disallow: / User-agent: OAI-SearchBot Allow: / These are separate crawlers with separate purposes. In our sample, 839 of the 945 sites that block GPTBot, or 88.8%, still allow OAI-SearchBot. That is a deliberate and useful distinction if your goal is to opt out of training while remaining eligible to be cited in ChatGPT search. The same pattern appears across AI labs. ClaudeBot is explicitly blocked by 10.1% of the 9,037 tools, while Claude-SearchBot is blocked by just 0.1%. Google-Extended is blocked by 9.9%, but its purpose is also distinct from ordinary Google Search crawling. Do not assume a broad-looking rule has the result you want. Check the actual crawler names and decide which capabilities you want to permit. A safe way to review your file Start by opening the public URL: https://your-domain.example/robots.txt Then look for three things: A named crawler group, such as User-agent: GPTBot . A Disallow: / directly inside that group. A wildcard group, User-agent: * , that could affect all crawlers. Our measurement only counts a site as blocking GPTBot when the named GPTBot group itself contains Disallow: / . This matters because ordinary technical exclusions are widespread. Only 31 sites in the 9,037-site sample, or 0.3%, block every crawler outright. Meanwhile, 44% have a path-level Disallow rule in a wildc

Author: Ignacio Paz An AI agent is only as useful as what it can remember - and how well it can connect the things it remembers. Most teams hand their agent a memory by reaching for a vector store: embed everything, retrieve by similarity, hope the relevant context comes back. It works, until you notice the agent keeps surfacing things that are near the question but not actually connected to it. Cobrainer , a skills-intelligence company based in Munich, took a different route. They gave their AI agent a memory that lives in the database as a graph, where the agent builds the relationships between nodes as it goes. They did it without adding a graph database, a vector engine, or a search engine to their stack. It all runs on SurrealDB, alongside a Rust-native agentic graph RAG built on the same store. Here's how, and why a single engine made the difference. The problem with flat memory Cobrainer runs a skills-intelligence platform - the kind of system that reasons about how people, roles, skills, and capabilities relate to one another. That's an inherently graph-shaped problem. But their first retrieval setup wasn't graph-shaped at all. It pulled context through flat vector retrieval over an S3-and-OpenSearch pipeline, which carried two recurring costs: Accuracy . Flat vector matches returned context that was loosely related - semantically near, but not necessarily connected in any meaningful way. The team wanted the agent to follow real relationships between entities, so its answers were grounded rather than approximate. Tokens . Broad vector matches meant stuffing a lot of marginally relevant context into every prompt - expensive, and more so with every call. The team wanted to fetch only the context that mattered. The obvious fix - adding a graph database on top of the vector and search systems they already ran - would have meant more infrastructure to operate. For a startup moving fast, that fragmentation was the thing to avoid, not embrace. What they wanted inst

Railway's pricing trips up developers who assume it works like Vercel or Netlify with a generous free tier and predictable monthly bills. It doesn't — it's usage-based, and the bill can climb fast once you move past hobby projects. Here's what you're actually paying for in 2026. How Railway pricing actually works Railway bills on three axes: compute (vCPU-hours), memory (GB-hours), and egress (GB). There's no seat-based pricing and no per-project fee. Every service you spin up — a Next.js app, a Postgres instance, a Redis container, a background worker — accumulates compute and memory cost independently. This is the first gotcha: a monorepo with three separate Railway services isn't one bill line, it's three. A typical Next.js + Postgres + Redis stack is billing on all three simultaneously, 24/7, even if traffic is zero. Railway free tier limits in 2026 The Hobby plan starts at $5/month (a recent change from the old credit system) and includes a $5 usage credit each month. If your services stay within that credit, you pay only the base $5. Exceed it and Railway charges the overage at standard rates. Resource Hobby included Rate above included vCPU ~8 vCPU-hours/mo (within $5 credit) $0.000463 / vCPU-second Memory ~32 GB-hours/mo (within $5 credit) $0.0000018 / GB-second Egress 100 GB/mo $0.10 / GB Postgres storage 1 GB (Hobby DB) $0.25 / GB / mo Execution timeout 10 min (one-off jobs) — Team members 1 (solo) — The credit math is easy to exhaust. A single 512 MB Node.js service running continuously costs roughly 512 MB × 3600 s × 24 h × 30 d × $0.0000018/GB-s ≈ $2.40/month in memory alone. Add a small Postgres instance and a Redis container and you've eaten the credit. The $5 base is essentially the minimum floor, not a ceiling. The free trial (no credit card) gives $5 one-time credit and then stops. That's roughly enough for a weekend of testing — not a production staging environment. Pro plan: what changes The Pro plan costs $20/month per workspace and removes the

Engineering a Digital Canon: Interactive Taxonomies for Over 40 Classical Zen Texts Preserving sacred literature and philosophical treatises online often suffers from poor structure, fragmented PDFs, and broken navigation. To solve this for classical Chan (Zen) Buddhism, we engineered chanzong.space (禅宗知识库) — a performant, open-access knowledge base built with Next.js 14, React 18, and D3.js. Whether you are studying the non-duality of the Platform Sutra or the intricate psychological analysis of Yogacara (唯识) mind theories, navigating multi-layered canonical texts requires modern web tooling. 🏛️ 1. Multi-Dimensional Canon Architecture Unlike a basic eBook reader, chanzong.space treats philosophical literature as a multi-relational graph: Foundational Classics (核心经典) : Platform Sutra (六祖坛经) : The fundamental teaching of direct seeing into one's true nature (自性顿悟). The Blue Cliff Record (碧岩录) : The pinnacle of Song Dynasty Koan commentary. Diamond Sutra (金刚般若波罗蜜经) : The ontological grounding of non-abiding mind (应无所住而生其心). Eight Verses on Eight Consciousnesses (八识规矩颂) : Master Xuanzang's indispensable guide to transforming consciousness into wisdom (转识成智). D3.js Dynamic Knowledge Graph : Spanning 500+ nodes (Patriarchs, Core Doctrines, Cultivation Methods, and Koans). Explore live in your browser: Global Zen Knowledge Topology . ⚡ 2. Technical Stack & Clean Typography To honor the contemplative nature of reading ancient texts, our frontend adheres to the rice-paper aesthetic ( bg-[#FAF9F6] ) paired with dark night sky navigation: Framework : Next.js 14 (App Router) + TypeScript + Tailwind CSS. Fast Search : Instant Ctrl+K global dialog searching across 40+ books, 160+ philosophical concepts, and 200+ koans. Vernacular Modern Commentary : Every chapter is paired with exclusive modern Chinese analysis and keyword glossaries, bridging ancient idioms into practical psychological insights. Offline Reliability : Full PWA Service Worker caching for distraction-free reading

Every messenger you use has a hidden dependency: a working network path to a datacenter. Drop into a basement, a packed stadium, a moving train through a tunnel, an exam hall with jammers, or a remote area with no plan, and the app is just a spinner. The people you want to reach are often standing a few meters away, but your message still has to travel to a server on another continent and back. When that path is gone, so is the app. Kabootar is my attempt to remove that dependency entirely. It is a messenger with no backend at all. Your phone forms a peer-to-peer mesh with other phones nearby, and messages hop device to device over Bluetooth and Wi-Fi until they reach the recipient. No internet, no servers, no SIM. It is built in Flutter, and the routing core is plain Dart. The core idea: delay-tolerant networking The insight that makes this work is refusing to assume the recipient is reachable right now . Normal networking is connection-oriented: open a path end to end, then send. If there is no path, there is no delivery. Kabootar instead treats the network as a delay-tolerant network (DTN). A message does not need a live end-to-end path at the moment you hit send. It needs a chain of carriers that will exist over time . You hand your message to whoever is nearby. They hold onto it, carry it as they walk around, and pass it along to the next phone they meet. Eventually a carrier bumps into the recipient and the message lands, even if that is minutes later and both you and the recipient have long since walked away. This is store-and-forward, the same shape as a durable, at-least-once message queue, except the queue is running across a swarm of phones instead of inside a datacenter. How a message actually travels The routing strategy is epidemic routing: flooding. When you send a message, it spreads to everyone in range like a rumor. Each device that receives it re-broadcasts it onward, so the message replicates through the crowd, taking every path at once. That red

Picture, you join a new team working on a big system. Everybody who knew anything has left, either to find greener grass or to enjoy a well deserved pension. You and the team struggle to build new features for the system or to adapt functionality to match changes in legislation. Not to mention the trouble it is to figure out what to fix when things go wrong. At the same time, the business that you support is screaming for innovation and pushing for more and more changes. Recognize this situation? Ever experienced it yourself? A world full of legacy systems “Legacy. What is a legacy? It’s planting seeds in a garden you never get to see.” – Lin-Manuel Miranda, “Hamilton” Legacy, the thing that you are remembered for, typically the word has a positive meaning… how come that in tech the word “Legacy” has such a bad connotation? When we call out a legacy system, we usually mean: code without tests ( Michael Feathers ) or code you “got” from somebody else, or code that you’re scared to touch. However, there is a reason these legacy systems are still around. In almost all cases, that system still brings in money or is somehow still valuable. If it did not bring any value anymore, wouldn’t it be decommissioned? There must be something in these systems that makes them survive, where other systems did not. How systems become “Legacy” So legacy systems are those that have become hard or scary to change. In my experience, that not because something is wrong with the code or technology. The major contributing factor is usually that the knowledge about the system has left the organization. And then I don’t mean the documentation, but the people that built, maintained and ran the system. When those people are gone, you know that nobody else is going to be happy touching that thing. The value of software Code is like a mapping of desired real world behavior to a program that can be executed by a machine. So where is the value of a system, is that in that code? Over the past years I

The science curriculum the industry is offering to schools casts plastics as safe, recyclable, and a source of good jobs. Critics say it’s propaganda.

Every framework, every job posting, and about half of LinkedIn wants to tell you what an "AI agent"...