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Yelp has launched Training Orchestrator. This new internal framework replaces individual team Spark training scripts. Now, it uses a configuration-driven, DAG-based execution model. By Claudio Masolo

The short answer is: while OpenAI Sora offers unmatched visual quality and physics rendering, it remains restricted behind a paid subscription structure. For creators who want a completely free, unlimited AI video generator, the newly released open-source LTX Desktop app by Lightricks allows you to run the LTX-2.3 video model locally on your own computer with zero usage costs or filters. The AI Video Paywall Frustration If you have tried building AI video content for YouTube, TikTok, or social marketing, you know how expensive it is. Platforms like Runway Gen-3 and Luma Dream Machine charge by the second. A simple five-second clip can cost up to fifty cents in API credits, making creative experimentation almost impossible for solo developers. OpenAI Sora is a powerhouse, but its high computational overhead means it will likely remain a premium, paid tool for the foreseeable future. To bypass this, our dev team set up the new open-source LTX Desktop application on our local workbench to see if local video generation is actually viable for production. Here is our hands-on review. |---|---|---|---| | OpenAI Sora | Closed Cloud | None (Paid Plan) | Cloud-Only | Cinema-grade physics, long multi-action shots. | | Runway Gen-3 | Closed Cloud | Daily Free Credits | Cloud-Only | Cinematic camera pans, high texturing quality. | | Wan2.1 | Open Weights | Free Hugging Face Spaces | 16GB VRAM (Local) | Photorealistic human movement, natural lighting. | | LTX-2.3 | Open Weights | LTX Desktop (Free) | 8GB VRAM (Local) | Fast generation speeds, local desktop interface. | Running Video Models Locally: The LTX Desktop Solution LTX Desktop, developed by Lightricks, is a standalone, open-source desktop application that lets you run their LTX-2.3 video generation model on consumer-grade graphics cards. Why LTX Desktop is a Game-Changer Low VRAM Footprint: Unlike HunyuanVideo or Wan2.1 which require massive 16GB-24GB VRAM cards to compile locally, LTX-2.3 is highly optimized and runs com

Members of the Army received an email informing them that they were rapidly depleting their AI tokens, and needed to limit use.

Once a quirky bastion of amateur vulva jewelry and pet portraits, Etsy is now deluged with mass-produced goods and AI knockoffs. Some customers don’t seem to mind.

If you have written any optimization code, you have met linear programming even if nobody called it that. "Maximize output without blowing the resource budget" is an LP problem, and the classic algorithm that solves it is the simplex method. It is worth understanding not because you will hand-code it (you'll usually call a solver), but because knowing how it moves makes you far better at modeling problems for it. Here is the algorithm stripped down to its logic. The problem shape Every LP problem has three parts: an objective function to maximize or minimize, e.g. Z = 5x1 + 4x2 a set of linear constraints, e.g. 6x1 + 4x2 <= 24, x1 + 2x2 <= 6 non-negativity: all variables >= 0 Geometrically, the constraints carve out a feasible region (a polytope). The optimum always sits at a corner of that region. The simplex method is just a smart way of hopping from corner to corner, uphill, until there is no higher corner to move to. The algorithm as pseudocode build initial tableau (add a slack variable per <= constraint) loop: compute Cj - Zj for each column if all (Cj - Zj) <= 0: break # optimal reached pivot_col = column with most positive Cj - Zj # entering variable ratios = RHS / pivot_col entries (only positive entries) pivot_row = row with smallest non-negative ratio # leaving variable pivot(pivot_row, pivot_col) # elementary row operations return solution from final tableau That's it. Four moves per iteration: score the columns, pick the entering variable, run the ratio test for the leaving variable, pivot. Repeat until the optimality condition holds. A quick worked run Take Maximize Z = 5x1 + 4x2 subject to 6x1 + 4x2 <= 24 and x1 + 2x2 <= 6. Add slack variables s1, s2, build the tableau, and iterate. The optimum lands at x1 = 3, x2 = 1.5, Z = 21. Two pivots and you're done. Simple on paper until the numbers get ugly. Where humans (and debugging) actually break The algorithm is clean. The arithmetic is not. A single wrong entry in one pivot silently corrupts every table

You've deployed your app to Kubernetes. The pod starts — then it gets killed. Or it's running but no traffic reaches it. Or it takes 90 seconds to initialize and gets restarted in a loop. Every one of these problems traces back to the same root cause: misconfigured or missing health probes . Kubernetes gives you three types of probes: livenessProbe , readinessProbe , and startupProbe . Each serves a different purpose. Mix them up and your pods restart in infinite loops. Get them right and your deployments self-heal, scale correctly, and handle rolling updates without a single dropped request. Here's what each probe does, when to use it, and how to configure it for a real production service. 1. Liveness Probe: Is the Container Alive? The liveness probe answers one question: "Is this container still running correctly?" If the probe fails, kubelet kills the container and restarts it. livenessProbe: httpGet: path: /healthz port: 8080 initialDelaySeconds: 5 periodSeconds: 10 failureThreshold: 3 Use liveness probes for deadlock detection . If your app enters a state where it's alive but not making progress (a goroutine leak, a stuck mutex, an infinite loop), the liveness probe exposes that and triggers a restart. The #1 mistake people make: using the liveness probe to check external dependencies like databases or upstream APIs. Don't do this. If your database is down and your liveness probe fails, Kubernetes will restart your pod — but the database is still down. Restarting the app doesn't help, and now you have a crash loop on top of a DB outage. That's worse. Liveness probes should only check internal process health. Not database connectivity, not Redis, not upstream services. 2. Readiness Probe: Is the Container Ready for Traffic? The readiness probe answers: "Should this pod receive traffic?" If it fails, the pod is removed from all Service endpoints. It is not restarted. readinessProbe: httpGet: path: /ready port: 8080 periodSeconds: 5 failureThreshold: 2 successThre

The openFDA enforcement API is free, keyless, and well documented on the surface. It is also full of failure modes that return HTTP 200 with quietly wrong data. Every number and error string below was measured against the live API on 2026-07-20; anything I could not reproduce has been cut. Pick the right endpoint first There are four recall-shaped endpoints and they are not interchangeable. Choosing wrong gives you a different universe of records with no warning. Endpoint Records What it is drug/enforcement.json 17,793 Recall Enterprise System (RES) drug recalls device/enforcement.json 39,519 RES device recalls food/enforcement.json 29,224 RES food recalls device/recall.json 58,756 CDRH device recall database, a different schema entirely The three enforcement endpoints share their schema. device/recall.json does not: its fields include cfres_id , product_res_number , k_numbers , root_cause_description , event_date_posted , event_date_terminated and recall_status , and it has no classification field at all ( count=classification.exact returns HTTP 404 "Nothing to count" ). If you are filtering for Class I, you want an enforcement endpoint. The two families also refresh on different clocks. On 2026-07-20 the three enforcement endpoints reported meta.last_updated of 2026-07-08, while device/recall.json reported 2026-07-17. The OR bug that silently returns the wrong answer This is the single most expensive trap, and it is undocumented. An unparenthesized OR discards every clause except the last one. All figures below are from food/enforcement.json . search=classification:"Class I" OR state:"CA" returns 4,003 - exactly the count for state:"CA" alone. Reverse the operands and you get 12,809 - exactly classification:"Class I" alone. Wrap it: search=(classification:"Class+I"+OR+state:"CA") returns 14,822 in both orders. That is the real union (12,809 + 4,003 - 1,990 overlap, and the AND of the two clauses does return 1,990). No error is raised in any case. The nastier varia

California and XPRIZE competition tests whether drones can stop wildfires early.

Vi um post legal esses dias sobre rodar duas contas do Claude Code na mesma máquina compartilhando tudo entre elas: mesmas skills, mesmos servidores MCP, mesmos hooks. O artigo era: "Um cérebro, duas carteiras" Eu rodo, exatamente o oposto, e acho que pra muita gente o oposto é a escolha certa. Minhas duas contas não são uma pessoal e uma reserva pra quando os créditos acabam. Uma é pessoal, outra é de trabalho. A última coisa que eu quero é meus MCP servers de trabalho, meus hooks de trabalho e meu histórico de projeto de trabalho vazando pras sessões pessoais. Então em vez de ligar as duas, eu mantenho elas separadas de propósito . O setup inteiro O Claude Code guarda o estado num diretório de config ( ~/.claude por padrão) e lê a variável CLAUDE_CONFIG_DIR pra apontar pra outro lugar. Esse é o único mecanismo que você precisa. Sem shim, sem symlink, sem jq . bash # ~/.zshrc # Claude Code: contas isoladas (pessoal vs trabalho) # Cada uma usa um CLAUDE_CONFIG_DIR proprio -> credenciais/sessao separadas. # ~/.claude = pessoal (default) # ~/.claude-work = trabalho claude-work () { CLAUDE_CONFIG_DIR = " $HOME /.claude-work" claude " $@ " ; } claude-personal () { CLAUDE_CONFIG_DIR = " $HOME /.claude" claude " $@ " ; } # 'claude' sozinho continua sendo a conta pessoal. source ~/.zshrc claude-work # pede login OAuth da conta de trabalho, uma vez só Usei funções de shell em vez de alias por um motivo: a função repassa "$@" limpo, então claude-work --resume abc e claude-work chat funcionam sem a variável de ambiente vazar pra nada mais no shell. Um alias faria quase o mesmo aqui, mas a função deixa a passagem de argumentos explícita. É isso. claude puro é pessoal. claude-work é trabalho. Nada é compartilhado, e é aí que mora a graça. Por que eu não compartilho o cérebro A versão de cérebro compartilhado faz symlink de skills , plugins , settings.json e dá merge no bloco mcpServers entre as duas contas pra elas se comportarem igual. Se as suas duas contas são de fato a mesm

Early Access games create a documentation problem that ordinary wikis do not handle well: the facts can change faster than search results, community posts, and copied tables are updated. A page can look polished and still be wrong for the current build. I have been working on an independent Subnautica 2 player wiki, and the most useful engineering lesson has been to treat every guide, map marker, and item row as versioned data rather than timeless prose. This post describes the workflow without assuming any particular framework. 1. Put provenance next to the fact For every structured record, keep at least: the game build or patch it was checked against; the source type: official note, in-game observation, or community report; the observation date; a confidence state such as verified, provisional, or disputed; a stable identifier that survives display-name changes. A user should not have to trust a page because it looks complete. They should be able to see whether a coordinate came from the current build and whether another player can reproduce it. 2. Separate stable identity from mutable labels Names, descriptions, recipes, and locations may change. Use an internal key as the identity and keep display text as versioned attributes. This prevents an item rename from creating a second logical entity or breaking every inbound link. The same rule helps with localization: English and translated labels point to one entity, while the source and verification state remain shared. 3. Model maps as evidence, not decoration An interactive map should not be a pile of pins. A useful marker contains coordinates, category, build, evidence, verification state, and a short player-facing note. If a patch moves or removes the object, preserve the history and mark the old observation as superseded. This also makes filters honest. “Show verified markers for the current build” is a meaningful query; “show everything ever imported” is not. 4. Make guides depend on structured facts Low-spoil

Alphabet, Google's parent company, is reportedly working on a new chip designed to make its Gemini models run much more efficiently.

The agency stressed that the July 17 voluntary recall from Taylor Farms still stands. The supplier has been linked to the ongoing cyclospora outbreak.

X says the rebuilt version of its Android app is now available globally.

AT&T suffers setback but will keep asking court and FCC to preempt state rules.

A simple tutorial series on go in Hinglish. Jitna mujhe aata hai utna, aur zyada tar mere notes se! Note: Me non-native speaker hu to spelling mistakes to honge, is liye Hinglish spellings aur grammar ke liye main AI use karunga, par dev ke bot ko pata nahi chalega kyuki ye English nahi hai 😉 Parichay / Intro Shuruat hum isse karenge ki Go kya hai aur uske fayde kya hain. Pehle hum Wikipedia ki definition dekhte hain: To, Go ek aasan language hai. Go compiled language hai, matlab ek bar compiler ko humne apna code diya to wo ek executable file dega jo sidha hum chala sakte hain bina kisi aur dependency ke, jaise ki Python ka interpreter. Dusri high-level languages jaise Python ya JavaScript se Go tez chalta hai. Garbage collected hai, yani C ki tarah hame memory ko manually manage karna nahi padega. Go ka istemal kahan hota hai? APIs aur Web Servers banane me Network Programming aur Distributed Systems me Cloud-Native Applications aur Microservices banane me DevOps aur Infrastructure Automation me Command Line (CLI) Tools banane me Agar upar ki baatein abhi samajh na aayein, to koi baat nahi. Filhal itna samajh lijiye ki Go ka istemal bahut jagah hota hai to job ke liye kaam aayega. To ye thi Go ki kahani, CSM ki zubani... (jo chalti rahegi!) Aur haan, har post ke aakhir me meri ek shayari hogi, jo aapko guru dakshina ke roop me jhelni padegi! 😄 ख़यालों को बातों में उलझाए रखना । इस पल के हक़ीक़त को सुलझाए रखना । शिद्दत से चाहा जो मिल कर रहेगा । चेहरे पे मुस्कान थोड़ी बनाए रखना ।। -csm

Microsoft PowerToys includes a Hosts File Editor. It is free, signed, and already on many Windows machines. It is a good editor. It is not always a good hosts workflow . What PowerToys Hosts does well Opens the real Windows hosts file with the right elevation story Simpler than hunting C:\Windows\System32\drivers\etc\hosts in Notepad Free if you already use PowerToys Fine for a handful of static lines When people search for an alternative You switch environments all day Local shop in the morning, client staging after lunch, cutover IP at night. An editor with one big file turns into commented chaos. You want named profiles you can toggle, not archaeology in comments. You also use a Mac or Linux box PowerToys is Windows-only. Your hosts process should not fork by OS if the team shares domain names. You forget ipconfig /flushdns Same bug as every other hosts tool without auto flush: file correct, browser wrong. Alternatives on Windows SwitchHosts Free, open source, profiles, also runs on Mac/Linux. Best PowerToys alternative when you need environment switching and maybe multi-OS later. Locahl Paid one-time. Windows, macOS, Linux. Automatic DNS flush and backups. Best when PowerToys feels too manual and you want the apply step to include flush + safety. Notepad as Administrator Still works. Still easy to save the wrong copy or skip flush. Only fine for rare edits. Hostly / CLI hosts switchers Interesting if you want hosts open Dev from scripts. Check that the project is maintained before you depend on it in CI. Feature snapshot (Windows view) Tool Profiles Auto flush Multi-OS Cost PowerToys Hosts Limited No No Free SwitchHosts Yes No Yes Free Locahl Yes Yes Yes One-time Notepad Admin No No Manual Free Practical upgrade path Keep PowerToys for now if you only have 5 stable lines When you start commenting / staging blocks every week, move to SwitchHosts or Locahl Always backup before the first import After every apply: ipconfig /flushdns ping myapp .test If PowerToys is

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Over the weekend, several current and former advisors to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks, the president’s AI and crypto “czar” until…

The lawsuit from the states alleges that the deal would harm movie theaters, basic cable distributors, and audiences.

A team lead asks the question in a planning meeting and the room splits three ways: do we build this agent in Copilot Studio, write the orchestration ourselves and host it, or hand our container to Foundry and let it run our code? All three are official Microsoft build paths in 2026, all three end up in the same tenant-wide agent inventory, and the wrong pick costs you a rebuild once the project outgrows it. The answer is not "the most powerful one." It is the one whose service model matches who is building the agent, who owns the runtime, and how much pro-code control over orchestration and protocols you actually need. This article is the decision framework for that choice, grounded in Microsoft Learn and current as of mid-2026. Two of these three paths are public preview, so this is a guide to architectural fit and direction, not a production-reliability scorecard. TL;DR Three build paths, picked by service model, not power. Copilot Studio: low-code managed SaaS for makers. GA. Foundry Hosted agents: managed PaaS runtime for your own container. Public preview. Microsoft 365 Agents SDK: pro-code, self-hosted, widest channel reach. Agent Framework orchestrator in public preview. Monday move: before picking a platform, write down four things for this agent - who builds it (maker or pro-dev), who must own the compute, what channels it has to reach, and whether you need custom protocols or background/async behavior. Those four answers pick the path more reliably than a feature checklist. The three paths in one paragraph each Microsoft's own Cloud Adoption Framework frames the build options as three service tiers, which is the cleanest mental model to start from. The CAF positions them as Copilot Studio (SaaS, no/low-code), Microsoft Foundry (PaaS, pro-code or low-code), and GPUs and Containers (IaaS, code-first frameworks for maximum flexibility). The first two are managed by Microsoft. The third is where the self-hosted SDK path lives when you own the compute end to e

Every time you run a JavaScript program, a lot happens behind the scenes. Variables are allocated memory, execution contexts are created, functions are pushed onto the call stack, and the engine starts executing your code. But before we dive into all of that, let's first understand what JavaScript actually is and why it was created. An Introduction to JavaScript What is JavaScript? JavaScript is a programming language that was originally created in 1995 by Brendan Eich in just 10 days while he was working at Netscape. JavaScript is a high-level programming language primarily used to make web pages interactive. Today, it is also used to build servers, mobile applications, desktop software, and much more. Why was JavaScript created? JavaScript was created to make web pages alive . But what does "alive" mean? it means adding interactivity (e.g., animations, clickable buttons, popup menus, etc.) to the static web pages. Today, JavaScript isn't limited to browsers. With runtimes like Node.js, it can also be used to build backend applications and APIs, which allow you to add more functionality to a website. Did you know? When JavaScript was created, it initially had another name: “LiveScript”. Where can JavaScript be used? In your browser — every interactive website uses it (Facebook, YouTube, Gmail). On servers — through Node.js, you can build backend APIs. In mobile apps — using frameworks like React Native. In desktop apps — VS Code itself is built using JavaScript (Electron). In smart devices, games, robots, and much more. Now that we know what JavaScript is, another question comes to mind: How does JavaScript execute my code? Before answering that, let's first understand Who executes my code? . The answer is: The JavaScript Engine The JavaScript Engine We already know what JavaScript is, but what exactly is this engine ? The "Engine" A JavaScript engine is a piece of software responsible for executing JavaScript code. Every environment that runs JavaScript, whether i