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At least three of the first 32 Rassvet satellites likely suffered outright failures, and more may be in trouble.

Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints. By Ricardo Ferreira

The idea of humanoid robots running our homes has been the stuff of science fiction for years. But unlike hoverboards and self-tying sneakers, a robot housekeeper has never felt close to rolling into our homes. Until now. This has been the year of the humanoid robot. At CES, there were dozens of human-shaped bots on […]

Comcast has added motion detection as a feature to its wireless routers: The feature sends push notifications to users when motion is detected near a connected device, such as a TV or printer. It has different settings for when people are home, asleep, or away. The Xfinity app also lets users see live motion activity and a feed of recent activity. Comcast acknowledges that the system has some limitations. Home size, layout, building materials, and the placement of the router and connected devices can all affect its ability to detect motion. Comcast says it does not guarantee its performance...

Build a Polymarket mean reversion bot in Python using price history, z-scores, order-book data, execution controls, and production risk management. Introduction A Polymarket mean reversion strategy is based on a simple quantitative hypothesis: when an outcome-token price moves unusually far from its recent statistical range, it may eventually move back toward its local mean. The difficult part is not calculating a moving average. The difficult part is determining whether the deviation is actually temporary—or whether new information has permanently changed the market's fair probability. For a trading bot, that means the strategy needs three separate components: A statistical signal. An executable market-data layer. An execution and risk-management system. Polymarket's current API exposes order-book snapshots and historical price data, making it possible to build the research layer directly from market data. ([Polymarket Documentation][1]) About the Author Bo$onaX I write about Polymarket trading bots, prediction-market infrastructure, algorithmic trading, Python automation, Web3 development, and quantitative strategies. Contact: Github: github.com/n9xdev/poly-alpha-lab Telegram: t.me/bosonax Youtube: youtube.com/@bosonax X: x.com/xxniiinxx Gmail: mailto: dylandevera91928@gmail.com What You'll Learn How mean reversion applies to prediction-market prices How to calculate a rolling z-score How to filter false signals How to incorporate the CLOB order book How to structure a Python bot Why fees, spread, liquidity and adverse selection matter How to backtest and paper trade the strategy 1. The Mean-Reversion Model Let (P_t) represent the price of an outcome token. Calculate a rolling mean: { mu_t = SMA(P_{t-n+1},...,P_t) } and rolling standard deviation: { sigma_t = Std(P_{t-n+1},...,P_t) } Then calculate: { Z_t = \frac{P_t-\mu_t}{\sigma_t} } A basic strategy might define: Z < -2: unusually low price → investigate a long Z > +2: unusually high price → investigate an exit

If you see a Prop 65 warning, don’t fret. Here’s what you need to know.

1. Provisionar não é configurar No artigo anterior desta série, vimos o OpenTofu como uma alternativa (ou substituto direto) ao Terraform para a tarefa de provisionar infraestrutura — criar VMs, redes, bancos de dados gerenciados, buckets. Mas provisionar um servidor é só o primeiro passo: depois que a VM existe, alguém precisa instalar pacotes, configurar usuários, aplicar hardening, subir a aplicação e manter tudo isso consistente ao longo do tempo. É nesse espaço que o Ansible entra — e é comum ver os dois trabalhando juntos no mesmo pipeline, não como concorrentes. 2. Onde o Terraform para e o Ansible começa A distinção mais útil na prática é: Terraform (e OpenTofu) são ferramentas de provisionamento : elas conversam com APIs de nuvem para criar, atualizar ou destruir recursos. O modelo mental é declarativo e orientado a estado desejado do recurso : "quero uma VM com esse tipo de instância, nessa rede, com esse disco". Ansible é uma ferramenta de gerenciamento de configuração : ela conecta em máquinas já existentes (via SSH, sem precisar de agente instalado) e executa tarefas para deixá-las em um estado desejado: "quero o Nginx instalado, essa versão, esse arquivo de configuração, esse serviço rodando". Não é incomum ver os dois no mesmo pipeline: o Terraform cria a VM e expõe o IP como output; o Ansible usa esse IP para conectar e configurar o que está dentro dela. Um cuida do "hardware" (ainda que virtual), o outro do "software". 3. Conceitos fundamentais do Ansible Antes de ver exemplos reais, vale fixar o vocabulário: Inventory: a lista de máquinas que o Ansible gerencia, agrupadas logicamente (por exemplo, webservers , databases ). Pode ser um arquivo estático (INI ou YAML) ou gerado dinamicamente (ex.: a partir de tags de uma conta AWS). Playbook: um arquivo YAML que descreve, em ordem, quais tarefas ( tasks ) devem ser executadas em quais grupos de máquinas do inventory. Module: a unidade de trabalho executada por uma tarefa — existem módulos prontos para

Mithil Vakde trained a small transformer from scratch in 1.5 hours on a rented RTX 5090. The compute cost came to about 67 cents. The model scored 44% on the ARC-AGI-1 public eval, one point behind TRM's 45% and in the same band that left DeepSeek R1, o3-mini, and Gemini 2.5 Pro behind when the TRM paper ran the comparison ( arXiv 2510.04871 ). This is the third post in his ARC series. The previous result went viral and drew public scrutiny from researchers including Lucas Beyer, Jeremy Howard, and Rohan Anil. The new one raises the score, cuts the cost, and answers every objection in a long appendix. What the model does Each puzzle becomes a sequence of tokenized grids. The transformer trains on those sequences from scratch at test time, using both the train puzzles and the eval puzzle inputs, with the eval labels hidden. Test-time training is not a loophole here. ARC is a meta-learning benchmark built around skill acquisition, and Chollet's original paper frames the whole thing as a measure of how efficiently a system gains new skills ( arXiv 1911.01547 ). The architecture got a 2026 refresh: 8 layers, SwiGLU, RMSNorm, the NorMuon optimizer, 3D RoPE, and per-task embeddings. Inference augments each test input with color and dihedral permutations, inverts the augmentations, and submits the two most common outputs. The biggest change is the loss function. Previous versions trained on input and output tokens. This one trains on output tokens only, which makes the approach supervised. Score went from 40% to 44%. Vakde admits he does not fully understand why, and the training loss got worse even as scores improved. His guess is finite model capacity. The ablations back him up on what carries the result: drop the 3D RoPE and the score falls to about 24%, drop the per-task embeddings and it falls to about 24%. Representations, not compute, are doing the work. The lineage it joins None of these systems pretrain. They learn from the benchmark's roughly thousand examples: S

The problem: every "free" image converter wants your files If you've ever needed to quickly convert a batch of photos to WebP or shrink a folder of PNGs before shipping them to production, you've probably run into the same annoyance I did: most " free online converters " require you to upload your files to a remote server first. That's fine for a random screenshot. It's not fine when the images are: Unreleased product shots under NDA Client assets you're not supposed to redistribute Personal photos you'd rather not hand to a third-party server you know nothing about So I started looking at what the browser can actually do on its own — and it turns out, more than most people assume. What the browser can already do Modern browsers ship with everything needed to decode, resize, re-encode, and compress images entirely client-side: + toBlob() / toDataURL() for re-encoding to JPG, PNG, or WebP The File API for drag-and-drop and batch uploads Web Workers to keep the UI thread responsive during batch conversion JSZip (or similar) to bundle multiple converted files into a single downloadable ZIP None of this requires a backend. No image ever has to leave the user's machine. Why this matters beyond privacy Besides the obvious privacy win, doing conversion in-browser has some nice side effects: No server costs that scale with usage. A traditional image-conversion API has to provision compute for every request. A client-side tool scales for free — the user's own CPU does the work. No upload/download round trip. For large batches, skipping the network entirely is often faster than uploading to a server and waiting for a processed file back. Works offline once loaded. A PWA-style client-side converter keeps working even with a flaky connection. The trade-offs It's not free lunch: Very large batches (hundreds of high-res images) can strain the main thread if you're not careful with Web Workers. WebP/AVIF encoder quality and speed vary by browser engine, so you can't guarantee byte

The company told TechCrunch he is "pursuing new endeavors."

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After long delays, JLR’s biggest gamble with its Range Rover brand is here with huge performance, range, and off-road EV skills. It’s mighty pricey, but the US gets a cheaper model.

JioHotstar will only have entertainment content when it launches in the UK, Canada, and Singapore.

Google has reportedly been reaching out to a number of Hollywood's biggest studios, hoping to strike licensing agreements that would allow it to train its AI models on copyrighted material in exchange for massive piles of cash. In theory, these deals would be a win-win: a huge financial boon to the studios that would also […]

Anthropic says its newest AI models, Fable 5.1 and Mythos 5.1, address criticisms from customers about price, data retention, and overzealous safeguards. The company claims Claude Fable 5.1 offers stronger performance than Fable 5, but costs around 25 percent less typically and up to 45 percent less for complex agentic tasks, thanks to reduced pricing […]

While some of the features see Google playing catch-up to Apple, which already offers similar features for iPhone users, others specifically leverage Gemini to provide various improvements.

At 5:21 p.m. Eastern on Friday, June 12, Anthropic received a United States government directive. By nightfall, two of the most capable artificial-intelligence models in the world had gone dark after a Commerce Department export-control directive prompted Anthropic to take them offline worldwide. The directive required Anthropic to prevent access to Claude Fable 5 and Claude Mythos 5 by any foreign national, whether that person was inside or outside the United States. It extended even to Anthropic's own foreign-national employees. Because the company had no reliable way to verify every user's nationality in real time, it suspended both models for everyone . The controls remained in place until June 30. On July 1, Anthropic restored Fable globally. Mythos took a narrower path: after government approval on June 26, access returned to a set of United States organizations while Anthropic continued coordinating with the government over broader domestic and international access. That sounds like a short outage with a happy ending. It was neither. I have spent years building systems where a dependency going unavailable is not an abstraction. Once software is woven into a product or an operating process, access to it becomes part of the architecture. A model that can be withdrawn immediately under a company-specific determination and factual rationale no customer could have read is no longer only a technical dependency. It is a bet on unpublished policy. The United States needs to govern frontier A.I. It also needs companies, researchers, investors, and customers to know what the rules are before the government enforces them. June showed how far apart those two needs remain. The security concern was real The government's concern was not invented. Fable and Mythos shared the same underlying model, but Fable was released with strong safeguards for general use while Mythos, with fewer safeguards, went only to a small group of defensive-cybersecurity partners. Anthropic says My

Sonos unveiled a bunch of new stuff today at its open house event. There's the $699 Beam Ultra soundbar and the $449 Ace Ultra headphones, plus several under-the-hood app updates (some coming sooner than others). While the show floor was a less than ideal venue to judge audio quality of either new product, a private […]

Fable 5.1 includes changes meant to reduce token cost and false-positive restrictions from the model's safeguards.