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Most agent tutorials stop at tools: give the model a function schema, hope it calls the right one. That works for get_time and hash_string . It falls apart when the model skips a knowledge search and opens a ticket, or when eighty APIs all land in one context window. Solon AI keeps tools as the execution unit, then adds Talent as the product unit: tools plus SOP plus activation rules. Think of it this way: Tool ≈ a function Talent ≈ a class that owns those functions, their playbook, and when they appear This post is a practical map of when to stay on tools, when to wrap them in a talent, and how registration actually works in Solon v4.0.3. The product failure behind “just add more tools” Bare tools only answer two questions for the model: what can I call? what args does it need? They do not answer: should this capability even be visible right now? what order must I follow before a dangerous call? which tools belong to the same business domain? That gap shows up as: premature side effects (ticket created before diagnosis) context blow-up (full tool tables on every turn) weak SOP compliance (model freestyles across domains) Talent is Solon’s answer: a reusable package of awareness + instruction + tools , with automatic coloring so tools keep their domain identity. Tool vs Talent in one table From the official comparison: Dimension Tool ( FunctionTool ) Talent ( Talent ) Unit Single function / method Instruction + tool set + state Abstraction Physical: how Logical: when and under which SOP Context awareness Passive isSupported(Prompt) can activate or hide Injected content Tool schema (JSON) System prompt fragment + tool list Constraint strength Weak — model freestyles from description Strong — SOP via getInstruction Registration defaultToolAdd / toolAdd defaultTalentAdd / talentAdd They are not rivals. A talent contains tools. Registering a talent also registers its tools; you do not need a second defaultToolAdd for the same set. Lifecycle: what actually runs at reques

It’s “the most car-like Range Rover ever created,” but will this all-electric grand tourer spoil Jaguar’s Type 01 party?

submitted by /u/Expensive-Cookie-106 [link] [留言]

I've been building an AI that learns to play the classic Snake game through reinforcement learning. The goal is to reach high scores while keeping training time as low as possible. The current version averages 86 points (87 is the maximum) after less than 10 hours of training on a single free Google Colab T4 GPU. To keep training fast, it runs 4,096 Snake games directly on the GPU, combines GPU-native environment simulation with PPO + GAE, and uses a spatially-preserving CoordConv architecture that maintains the full game grid throughout training. I'm sure there's still room to improve. If you've worked on reinforcement learning or efficient training systems, what would you try next? Better exploration, reward design, network architecture, or something else? Repository: ( https://github.com/siddhartha399/PPO-CoordConv-Snake ) I'd really appreciate any feedback or criticism. submitted by /u/Due_Highlight_9341 [link] [留言]

submitted by /u/Happycodeine [link] [留言]

On Monday, District 9 and Gran Turismo director Neill Blomkamp unveiled his latest project: a 13-minute sci-fi short titled Nightborne that's loosely based on Peter Watts' 2014 novel Echopraxia. The short comes from Blomkamp's new AI startup / production company, Barley Studios, and features characters whose voices and faces are modeled after human actors. But […]

Meituan's LongCat team shipped a quiet but meaningful update to its open-source talking-avatar stack: version 1.5 swaps the audio encoder, distills sampling down to 8 steps, and adds an INT8 path to fit the model on tighter GPUs. Here's what actually changed under the hood, and which flags you now have to pass. LongCat-Video-Avatar 1.5: DMD2 distillation, Wav2Vec2 replacement, and INT8 offloading LongCat-Video-Avatar 1.5 is an audio-driven talking-avatar generator that turns one portrait plus an audio clip into a lip-synced video with head motion, expression, and body dynamics. Released May 21, 2026, it is an avatar head built on the 13.6-billion-parameter dense LongCat-Video diffusion transformer [base model, Oct 2025]. The headline change: v1.5 replaces the Wav2Vec2 audio encoder used in v1.0 (Dec 16, 2025) with Whisper-Large-v3, which the team attributes to smoother, more natural lip dynamics. Three new flags define the v1.5 workflow, none of which exist in v1.0: Flag What it does Notes --use_distill Enables DMD2-based step distillation, collapsing generation to 8 NFE Mandatory for v1.5 — omitting it falls back to the full-chain v1.0 sampling schedule, not an 8-step path --use_int8 Loads the 13.6B dense DiT in INT8 to cut VRAM pressure Main lever for consumer GPUs --resolution Selects 480P or 720P output New in 1.5 Under the hood, the technique report describes Cross-Chunk Latent Stitching, which removes redundant VAE decode/encode cycles between autoregressive chunks, enabling seamless minutes-long generation without re-encoding overhead [arXiv:2605.26486]. That matters for long-form output where earlier tools drift or stutter at chunk boundaries. One caveat worth flagging before you invest a GPU-hour: Meituan claims parity-or-better results versus HeyGen, Kling Avatar 2.0, and OmniHuman-1.5 across 508 image-audio pairs, 770 crowdsourced evaluators, and 13,240 judgments [human-eval benchmark]. That evaluation is entirely author-controlled and reported as win-rat

Durante anos fizemos a pergunta errada "Será que a IA vai substituir os desenvolvedores?" Hoje sabemos que essa não era a pergunta correta. A pergunta correta é: O que passa a ter valor quando escrever código deixa de ser caro? Isso muda completamente a engenharia de software. Por décadas, metodologias como Waterfall, Scrum, XP, DDD e Clean Architecture nasceram em um mundo onde escrever código era caro. Documentação envelhecia rapidamente porque reescrevê-la custava caro. Especificações eram abandonadas porque implementar consumia semanas. Então surgiu a IA. Pela primeira vez na história, produzir código ficou quase gratuito. O valor migrou. Código ficou barato. Julgamento não. Hoje qualquer LLM produz centenas de linhas de código em segundos. Mas ela não decide: qual problema resolver; quais regras de negócio existem; quais exceções importam; quais compromissos arquiteturais devem permanecer pelos próximos cinco anos. Essas continuam sendo responsabilidades humanas. O gargalo mudou Antes, escrevíamos código. Agora escrevemos decisões. Especificações. Arquiteturas. Critérios de aceitação. Revisões. A vantagem competitiva deixou de ser velocidade de digitação. Passou a ser clareza de pensamento. Por que o vibe coding não escala Conversas são uma péssima fonte de verdade. Cada prompt aumenta o contexto. Cada correção adiciona mais tokens. Cada interação obriga a IA a reconstruir sua intenção. Em algum momento ela deixa de raciocinar sobre o sistema e passa a raciocinar sobre a conversa. Esse é o verdadeiro custo escondido do vibe coding. Especificações passam a ser o centro do projeto Foi essa percepção que originou o Spec Driven Development . A ideia é simples: A conversa deixa de ser a memória do projeto. A especificação passa a ser. Cada funcionalidade nasce de uma spec, evolui para um plano, transforma-se em tarefas e somente depois é implementada. O resultado é previsibilidade. Menos retrabalho. Menos tokens. Menos ambiguidades. https://books.kodel.com.br/pt-br/

I've built a lot of voice integrations. Every single one needed a deployment whose only job was to talk to the Sinch API: receive the callback, translate it into SVAML or a Conversation API call, hand off to the rest of the application. Not the interesting part. Just the piece that has to exist because your code and Sinch's network live in different places, with a round trip between them on every call event. Sinch Functions moves that piece into the network itself. Your voice and messaging handlers run on the same infrastructure that's already carrying your calls and messages, right where the callbacks originate. This isn't a general-purpose Lambda replacement: the rest of your application, and any compute that isn't glue against Sinch's APIs, stays exactly where it is, whether that's AWS, Azure, or your own infrastructure. Your order lookup, your database writes, your actual business logic: still on Lambda or wherever you already have it. Functions is specifically for the code that exists only to bridge your app and the Sinch network, not a place to run your whole system. I've been trying it out and this post walks through getting set up, writing a basic voice function, and deploying it. Pricing is usage-based and separate from your Voice and Conversation usage: $0.10 per compute-hour (first hour free each month), $0.05 per GB-month of storage (first 0.1 GB free), $0.05 to $0.15 per GB-month for the database depending on tier (first 0.1 GB free), and $5.00 per month per function if you want it kept always-on instead of scaling to zero. These are the current Standard rates from your project's Billing Overview in the Sinch dashboard; treat them as a snapshot, not a guarantee, given the Alpha status below. Sinch Functions is listed as Alpha in the Sinch Build dashboard. Expect the CLI, runtime APIs, pricing, and this walkthrough itself to change before general availability. Treat it as something to experiment with, not something to put in front of production traffic y

The country's new law also bans the use of mobile phones in schools.

In the ever-evolving landscape of technology, software engineers continually seek optimal solutions to complex problems. To achieve this, a deep understanding of the underlying technologies is essential. When considering adopting new technologies, it's crucial to ask: Why : What problem does this technology solve? How does it align with our project's goals? What : What specific tools or frameworks can address our needs? How do they compare in terms of features, performance, and community support? How : How does the technology work? Understanding its inner workings can significantly enhance problem-solving and troubleshooting capabilities. The Benefits of Understanding Technology's Building Blocks Enhanced Problem-Solving : A deep understanding of a technology's fundamentals empowers developers to diagnose and resolve issues more efficiently. Effective Integration : Knowledge of a technology's architecture facilitates seamless integration with existing systems. Optimal Utilization : By grasping a technology's capabilities, developers can leverage its features to their full potential. Future-Proofing : A solid foundation in technology principles enables developers to adapt to emerging trends and innovations. A Case Study: Containers Containers, a popular virtualization technology, provide isolation and portability for applications. By understanding the underlying concepts of namespaces, control groups, CRI, and the Linux kernel, developers can effectively manage and troubleshoot containerized environments. In conclusion, the fast-paced world of software development, staying ahead requires a proactive approach to technology adoption. By carefully considering the "why," "what," and "how" of new technologies, software engineers can make informed decisions, optimize their projects, and deliver innovative solutions.

There are two versions of this job and they have different answers. Most of the confusion, including ours, comes from assuming they are the same thing. Applying a whole template list to a matter Clio's interface has a button for this, and the natural assumption is that there is a matching endpoint on the task template resource. There is not. Nothing under /task_template_lists will assign anything to a matter, and no path in Clio's spec contains "apply". It is done from the matter instead. Both POST /matters.json and PATCH /matters/{id}.json accept a nested array: task_template_list_instances[] : task_template_list : { id } required on POST assignee_id : the user the list is assigned to notify_assignees : whether assignees get notified due_at : ISO-8601 date (format : date, not date-time) Those are the only two operations in the API that accept it. Note the asymmetry: on POST /matters.json the task_template_list object is required, and on PATCH /matters/{id}.json the item schema marks nothing as required at all. POST /task_template_lists/{id}/copy.json exists and sounds like the thing you want. It is not. It duplicates a list into another list , takes name , description and practice_area (all optional), and has no matter parameter. The two problems that will actually cost you time Instances are write-only. task_template_list_instances appears in those two request bodies and nowhere in the matter response schema. So there is no documented way to ask "does this matter already have the list on it?" You verify by fetching /tasks.json filtered to the matter and matching on names, which is uglier than it sounds and is most of the reconciliation work. Which makes idempotency your problem. If your automation fires the PATCH twice, nothing in the API stops you assigning the checklist twice. For anything triggered off matter creation or a stage change, decide up front how you detect an already-applied list, because the readback above is the only tool you have. Between them, th

If you have ever stared at a misbehaving circuit at two in the morning, muttering about the "bug" you cannot find, you are part of a lineage that runs back to a very literal insect. On September 9, 1947, engineers working on Harvard's Mark II computer opened up the machine, found a moth wedged inside a relay, and taped it into their logbook. The note beside it reads: "First actual case of bug being found." It is one of the most charming artifacts in the history of computing, and it carries a surprisingly practical lesson for anyone who builds connected hardware today. What actually happened in 1947 The Mark II was an electromechanical monster: a room-sized calculator built from thousands of relays, switches that physically clacked open and closed to represent ones and zeros. On that September day the machine was producing errors, and the operators -- part of the U.S. Navy computing effort that Grace Hopper worked in -- traced the fault to Relay #70 in Panel F. Inside was a moth, its wings shorting across the contacts. They removed it, taped it into the logbook at 15:45, and recorded the now-famous line. That page, moth still attached, lives today in the Smithsonian's National Museum of American History. Hopper loved telling the story for the rest of her career, which is why her name is forever attached to it. She is often credited with coining "bug" and "debugging" because of that moth, but the honest history is a little different, and the difference is the interesting part. The word "bug" was already old Engineers had been calling faults "bugs" for decades before 1947. Thomas Edison used the term in an 1878 letter to describe the little glitches and "difficulties" that show up when a new invention meets the real world. By the early twentieth century "bug" was common shop-floor slang among electrical and telephone engineers. So the moth did not invent the word. What made the logbook entry so memorable is the joke buried in it: "first actual case of bug being found"

A few weeks ago I wrote about describing your site once so any AI can use it. Since then the thing I was hand-waving at ("agents will check out for users") stopped being hypothetical. OpenAI shipped ACP and Google shipped AP2, and they solve the same problem in almost opposite ways. I implemented the merchant side of both. Here are the field notes, because the differences aren't obvious until you're in them. The 30-second version ACP (Agentic Commerce Protocol, OpenAI + Stripe) is session-based. The agent drives a live checkout session on your server, like a headless cart. It powers ChatGPT's Instant Checkout. AP2 (Agent Payments Protocol, Google) is mandate-based. Your store signs a "here is the cart and the price" object; the buyer's agent signs a "I authorize this" object. It leans on verifiable credentials and now the FIDO Alliance. Same goal. Completely different shape. ACP: a checkout session you don't own the UI for ACP is five REST endpoints and a state machine. The agent creates a session, updates it (address, shipping, coupon), and completes it: POST /checkout_sessions create from line items POST /checkout_sessions/:id update (address, shipping, discounts) POST /checkout_sessions/:id/complete pay What you return is a CheckoutSession: line items, live shipping options, and totals broken out (subtotal, discount, fulfillment, tax, total). Money is integer minor units (330 = $3.30). Payment completes when the agent hands you a Shared Payment Token and you charge it through Stripe. The card never touches the agent. The mental model: it's your existing checkout, minus the browser. AP2: sign the cart, don't run the session AP2 has no session. The agent sends you an Intent Mandate ("a red basketball shoe, under $120"). You price it and return a Cart Mandate you have cryptographically signed: { "contents" : { /* a W 3 C PaymentRequest: items , total , currency */ }, "merchant_authorization" : "<RS256 JWT>" // iss , sub , aud , exp , jti , cart_hash } That JWT is a

Australia was the first country to issue a ban in late 2025, aiming to reduce the pressures and risks that young users may face on social media, including cyberbullying, social media addiction, and exposure to predators.

The elevated streaming experience is happening in collaboration with Prime Video.

submitted by /u/fagnerbrack [link] [留言]

"Maybe that's my responsibility, to live alone with the truth."

Over the past decade, streaming platforms competed by dominating individual formats like music, video, podcasts, or audiobooks. Now, as AI makes it easier to create, organize, and recommend content, those distinctions are fading, pushing companies like Spotify, Netflix, YouTube, and TikTok to become all-purpose entertainment destinations instead.

Sila's fresh $300 million round will help it make enough of its silicon-carbon anode material to power more than 100,000 EVs.