标签：#AI

Director Travis Knight is also the creative mind behind 2016's Oscar-nominated Kubo and the Two Strings .

Where AI incidents in legal actually come from, and what infrastructure (not policy) prevents them. Blake Aber · Predicate Ventures · 2026 The policy layer is table stakes. It isn't enough. When Sullivan & Cromwell apologized to a federal bankruptcy judge in April 2026 for AI hallucinations in a court filing, the firm's apology letter said the firm had policies. Safeguards existed. Those safeguards weren't followed. That framing, "the safeguard existed but wasn't followed," is how a policy failure gets described. But something more specific happened: a hallucination was generated, wasn't caught at generation time, wasn't caught at review time, and made it into a document that got filed. That's not a policy problem. It's an infrastructure problem. The distinction matters because it determines what you build next. What policy can and can't do Policy is a promise made before the event. A well-written AI acceptable-use policy says: don't submit output you haven't reviewed; verify citations before they go into a document; a human must approve anything client-facing. This works when the human executing the task has time, attention, and professional accountability in that moment. It fails when one of those is missing: a deadline, a junior practitioner, a late-night run. Policy can't: Verify a citation at the point of generation Flag output that has drifted below a confidence threshold Stop hallucinated text from appearing in a draft before a human ever sees it Detect when the underlying model is behaving differently than it was in testing Policy can: Set the expectation that review must happen Define who bears accountability when it doesn't Create a paper trail after the fact One of those is prevention. The other is compliance. What infrastructure does instead An AI harness layer operates at the point of generation, not at the point of review. This reflects a broader reality that production AI is mostly harness and very little model . For legal work specifically, three com

Why AI programs at PE portfolio companies stall at the same organizational seam, and what to do about it. Blake Aber · Predicate Ventures · 2026 There's a failure mode I've watched play out at enough portfolio companies that I've given it a name: the ownership dyad. It goes like this. The AI program is running. The product manager owns the roadmap (what the AI should do). Engineering owns the deployment (how it does it). Both parties are competent. Both are aligned on the goal. And the AI initiative quietly stalls anyway, usually somewhere between the promising pilot and the production system that was supposed to follow. The mechanism is diffuse accountability at the decision layer. What the dyad looks like in practice In the average portco planning meeting, the PM and the engineering lead sit across from each other. The PM has a change request: "The model is producing summaries that miss the key clause in contracts above a certain length. We should fix this." Engineering hears this and wants to know: is this a prompt change or a model change? Either requires scoping, and scoping requires the PM's input on acceptable behavior. So engineering asks the PM. The PM says "whatever's best technically." Engineering ships a prompt change. The next month, the same issue appears in a different context. The PM brings it back. Neither person is wrong. Neither person is slacking. The problem is structural: there's no single person who can describe (precisely and completely) what the AI should produce, evaluate whether it's producing it correctly, and approve a change to the system without requiring the other party's sign-off. The dyad looks like shared ownership. It functions as diffuse accountability. No one is in charge of the model's behavior. The failure mode at month nine Most portco AI programs that make it through a successful pilot still die quietly around month nine of production. The most common reason is not that the model got worse. It's that the harness around the m

[v0.3.0] - 2026-06-29 🚀 Added Checkpoint Mechanism — ReActCore introduces three checkpoints during streaming: content_ended (after text content), before_tool_calls (before tool calls), and after_tool_calls (after tool calls), enabling precise interception and state synchronization of the execution flow. Message Queue System — Added a new MessageQueue class in run.py , supporting async enqueue, drain, and remove operations. Users can now queue messages while the LLM is running; queued messages are sent automatically after the current task completes. The frontend introduces a QueueBar component to display queued messages, with CSS spinning animation, single-line ellipsis, and hover-to-delete functionality. Queue Message Merging — MessageQueue.drain_all() now merges consecutive messages with the same name into a single message, preventing fragmented user input when multiple queue entries share the same sender. Queue WebSocket Events — The execution event protocol introduces three new event types: message_queued , queue_drained , and queue_returned ( useRunWebSocket.ts ). The frontend processes queue state updates in real time. Stop & Queue Integration — When the user clicks Stop, pending queued messages are returned to the input box via queue_returned . Checkpoint stops cleanly clear the queue and automatically start the next message. System Notification Messages — Introduced the SystemMessage type (with notification role) to separate error messages from assistant content. Errors are now rendered as independent notification bubbles, no longer embedded within assistant message cards. tiktoken Real-Time Token Estimation — ReActCore initializes a tiktoken encoder on startup for real-time token counting during streaming. Unknown models fall back to o200k_base . 🔧 Improved Custom Model Name Auto-Complete — The model name field in ModelManager has been upgraded from Select to AutoComplete , allowing users to type custom model names not in the predefined list. Message Block T

Qwen's new image paper is easy to read as another benchmark bump. Qwen-Image-2.0-RL takes the existing Qwen-Image-2.0 model, runs a reinforcement-learning pass on top, and reports better scores: 57.84 on Qwen-Image-Bench, up 2.61 points from the base model. Its text-to-image arena Elo moves from 1115 to 1193. Its image-editing arena Elo moves from 1256 to 1349. Those are the headline numbers. They are not the useful part. The useful part is the training story underneath them. The paper is a good reminder that "just optimize the reward" is a dangerously incomplete sentence, especially when the model is not an LLM and the output space is a whole image. The model got better, but not by one simple trick Qwen-Image-2.0-RL is a post-training pipeline for a diffusion image model. In plain English: the base model already knows how to generate and edit images. The RL stage tries to steer it toward outputs humans prefer, including better prompt following, better aesthetics, better portrait fidelity, and more reliable editing. The team builds task-specific reward models. For text-to-image, those rewards cover alignment, aesthetics, and portrait quality. For editing, they cover instruction following and face identity preservation. Then they train with a GRPO-style setup adapted for flow-matching diffusion models. If you only squint at that, it sounds like the same broad recipe people use for language models: generate candidates, score them, push the model toward the better ones. The paper is more interesting because it shows how fragile that story becomes once you touch the actual training loop. The CFG detail is the first real lesson Classifier-free guidance, usually shortened to CFG, is one of those diffusion-model knobs that users mostly experience as "make the image follow the prompt harder." Under the hood, it changes how the model samples. The Qwen team tested three ways to use it during RL. Using CFG during both rollout and training made the images collapse into incohere

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The startup, Proception, is taking a unique approach to collecting training data to tackle one of the hardest problems in robotics: hands.

Burn-in is often overblown, but understanding the phenomenon is still important.

Pocket sells a $129 credit card-shaped puck, which sticks to the back of your phone, and promises unlimited recordings, transcriptions, and to-do items.

Quando comecei a trabalhar com aplicações descentralizadas há mais de uma década, lembro bem da frustração de pagar US$ 50 em taxas de transação para mover alguns tokens na rede Ethereum durante um pico de congestionamento. Era um problema técnico que ameaçava inviabilizar todo o ecossistema. Hoje, observo com entusiasmo profissional como as soluções de Layer 2 transformaram radicalmente esse cenário, abrindo portas para casos de uso que antes eram economicamente impraticáveis — especialmente aqui no Brasil, onde a tokenização de ativos e os pagamentos em stablecoins crescem em ritmo acelerado. O problema fundamental: o trilema da escalabilidade Para entender por que as soluções de segunda camada são tão importantes, precisamos compreender o trilema da blockchain proposto por Vitalik Buterin. Uma rede precisa equilibrar três pilares: descentralização, segurança e escalabilidade. O Ethereum, em sua arquitetura original, priorizou os dois primeiros, processando apenas cerca de 15 a 30 transações por segundo (TPS) na camada base. Para se ter dimensão, redes de pagamento tradicionais como a Visa processam milhares de transações por segundo. Quando o DeFi explodiu em 2020 e 2021, e novamente com o boom dos NFTs, a rede simplesmente não dava conta da demanda. As taxas de gas dispararam, e usuários comuns foram literalmente expulsos pelo custo. Em meus projetos de consultoria, atendi empresas brasileiras que desistiram de iniciativas Web3 justamente porque os custos operacionais inviabilizavam o modelo de negócio. A pergunta que sempre me faziam era: "Como cobrar R$ 5 de um cliente se a taxa da transação custa R$ 30?". A resposta estava — e está — nas camadas de segunda geração. Como funcionam as soluções de Layer 2 O conceito central das soluções de Layer 2 é elegante: em vez de processar todas as transações diretamente na blockchain principal (Layer 1), executamos a maior parte do processamento "fora da cadeia" e depois enviamos apenas uma prova compacta de volta para o

A CFO will not act on a number an LLM eyeballed. They will not act on a number the model "estimated" by reasoning over a usage dump. And they should not — because the moment a language model emits a dollar figure it computed itself, that figure is a guess wearing the costume of a fact. This is the design constraint behind databricks-cost-leak-hunter , the pilot skill of the databricks-pack v2 rebuild shipped in the claude-code-plugins marketplace ( PR #906 ). Given a live, authenticated Databricks workspace, it surfaces real cost leaks across four named categories, ranks them by monthly dollar impact, and emits a report a finance reader can act on. The marketplace validator graded it B (88/100, zero errors). The SKILL.md is 329 lines. The single most important thing in it is a rule the model is structurally prevented from breaking: the LLM never does the dollar arithmetic. Why not just let the agent read the bill and summarize it? Because that is exactly how you ship a confidently wrong cost report. Hand a model a few thousand rows of system.billing.usage and ask it for the top cost leaks, and it will give you a fluent answer. It will add DBUs. It will multiply by a price it half-remembers. It will round. Every one of those steps is a place the model can be plausibly, invisibly wrong — and the output reads identically whether the math is right or hallucinated. The failure mode of an LLM doing FinOps is not a crash. It is a clean, well-formatted, wrong number. The fix is architectural, not prompt-engineering. The model is allowed to decide what to look for and how to explain it . It is never allowed to be the calculator. The dollar primitive: confirmed, never estimated Every confirmed figure comes from the customer's own billing tables — system.billing.usage joined to system.billing.list_prices . Not a model estimate. Not a public price list. The number Databricks actually billed. That join is defined once, as a priced CTE, and reused by every category query. Usage i

Most of the time, when I need to inspect a complex JSON payload, I copy the raw string from my terminal or network tab, open a browser tab, and paste it into one of the many "JSON Formatter" sites that clutter the first page of Google. It’s a ritual we all do. We paste, we click "Format," and we wait. For small payloads, this is fine. But when you are debugging a massive API response, a deeply nested configuration file, or a large dataset, that ritual breaks down. The browser freezes. The site asks you to upload a file. Worse, many of these tools send your data to a server for processing. If that JSON contains API keys, user PII, or internal schema definitions, you are essentially trusting a third-party service with your proprietary data every time you hit "pretty print." I got tired of the latency and the privacy overhead. So I built JSONForge . The core premise is simple: do everything locally. No server-side processing. No file uploads. No network requests for the core logic. Everything happens in your browser, powered by WebGPU for heavy lifting and a small model that runs in your browser for schema inference. The WebGPU Advantage JSON parsing is computationally cheap for a modern CPU, but rendering and diffing large structures is not. When you have a 5MB JSON file, the DOM manipulation required to display it as a tree view can cause significant jank. By offloading the parsing and formatting logic to the GPU via WebGPU, JSONForge handles massive payloads without blocking the main thread. You can open a file, click "Pretty Print," and see the result instantly, even if the file is hundreds of kilobytes or larger. The UI remains responsive because the heavy computation is parallelized on the graphics card. This also means the tool works offline. If you are on a plane, or your internet drops in the middle of a debugging session, your toolkit doesn’t vanish. You can continue to diff, validate, and format without interruption. Schema Generation Without the Server Roun

Omen AI raised a $31 million Series A to monitor chip coolant and stop bacterial outbreaks in data centers.

From knowing to doing — Phase 4 of the Jarvis AI Platform The Problem with Knowledge-Only AI After Phase 3, Jarvis could remember you across sessions and search your documents. But it still had a fundamental limitation. You: "What is the weather in Kathmandu right now?" Jarvis: "I don't have access to real-time weather data." You: "What is 2847 × 391?" Jarvis: "The answer is approximately 1.1 million." ← WRONG An AI that only knows things from training data is useful. An AI that can do things is transformative. That is what Phase 4 built. What Is a Tool Engine? A tool engine gives the AI model the ability to call real functions during a conversation. The flow looks like this: User: "What is the weather in Kathmandu?" ↓ AI Model ↓ "I should call WeatherTool" ↓ WeatherTool.getWeather("Kathmandu") ↓ "22°C, Clear sky, Humidity: 45%" ↓ AI Model ↓ "The weather in Kathmandu is 22°C and clear." The key insight: the AI decides when to call a tool and with what input . We don't hardcode "if user asks about weather, call WeatherTool." The model figures that out from the tool descriptions we provide. The Architecture Decision The most important architectural decision in Phase 4 was the package structure. ai . jarvis . tools / ├── JarvisTool . java ← marker interface ( root ) ├── ToolRegistry . java ← manages all tools ( root ) ├── builtin / ← built - in tools │ ├── DateTimeTool . java │ ├── CalculatorTool . java │ ├── WeatherTool . java │ └── WebSearchTool . java └── mcp / ← MCP protocol └── McpServerConfig . java Why not put tools inside ai/ ? The ai/ package handles HOW Jarvis talks to AI models. Tools define WHAT Jarvis can do. These are fundamentally different responsibilities. Mixing them would mean every new tool requires changes to AI infrastructure code. Keeping them, separate means adding a new tool requires exactly one file. The JarvisTool Pattern Every tool in Jarvis implements one interface. /** * Marker interface for all Jarvis tools. * Spring auto-discovers all @C

We (at DEV and MLH) are covering AI Engineer's World Fair by printing a physical newspaper called...

Ending any story is hard, but that's especially true of mystery-packed TV shows. Series like Lost initially hook viewers with constantly building secrets and questions, to the point that they can often seem incomprehensible. But the promise is that it will all pay off in the end - a feat that few shows ultimately manage. […]

Google was forced to cap Meta's use of Gemini AI due to a lack of capacity.

This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows. By Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic

FIFA says hydration breaks protect players from heat. They also create new annoying commercial breaks—and fans are calling foul.

You know what you want from a JSON document. You just don't want to memorize whether it's $[?(@.age > 18)] or $..users[?(@.active)] . Plain English in. JSONPath out. JSONPath Assistant on FormatList lets you paste JSON, describe what you need in natural language, and get a validated JSONPath expression plus the actual results — all in your browser. No account, no API key, no data sent to a server. How it works Paste your JSON — an API response, config file, or test fixture. Describe what you need — e.g. "get all user names" or "find products with tag tech". Generate — the assistant reads your JSON structure and maps your query to JSONPath. Validate & copy — the expression runs against your data immediately. Copy the path or the matched values in one click. If the first attempt returns no matches, the tool retries with a simpler variation automatically. Example queries You type Generated JSONPath Get all user names $.users[*].name Find users older than 18 $.users[?(@.age > 18)] Get names of active users $.users[?(@.active == true)].name Find products with tag tech $.products[?(@.tags.indexOf('tech') >= 0)] Get all order prices $.orders[*].price Get the first user's email $.users[0].email Get all pod names {.items[*].metadata.name} Get all pod IP addresses {.items[*].status.podIP} The tool ships with one-click examples for each of these — load one, hit Generate, and see how it works before trying your own JSON. What kinds of queries it understands Property access — "get all names", "list order prices" Numeric filters — "older than 18", "price under $10", "greater than 100" Boolean filters — "active users", "enabled devices" Tag / category searches — "with tag tech", "beauty category" Multi-condition — "both tech and mobile tags" Quantifiers — "the first user", "the last item" Kubernetes — "get all pod names", "pod IP addresses" It analyzes field names in your JSON — so users , products , orders , or whatever keys you actually have — and builds paths that match your sc