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Wispr Flow's new terms of service indicate it will introduce a notetaker that generates meeting summaries and action items.

As review season for NeurIPS wraps up, I have now reviewed for 3 major conferences this year. And I'm noticing a worrying trend: Out of the 12 papers I reviewed this year, only 1 provided full code (that runs the whole training pipeline from input dataset to output AUROC). 4 provided partial code with fragments of their method, but no ability to run the experiment end to end. And 7 provided no code. This is really bad for ensuring quality and reproducibility. Of the 5 papers that provided at least some code, 3 of them contained obvious bugs that completely invalidated the results. ML is highly technical and small bugs can have huge impacts if they are in the wrong place. Who knows what was going on in the remaining 7 papers. The fundamental issue here is of incentives: there is almost no cost to hiding code during the review process. Releasing code only increases odds of rejection due to reviewers finding bugs. The only way to fix this is to change the game by imposing real penalties on hiding code. submitted by /u/Flaky-Ambition5900 [link] [留言]

It was 2 AM, and the on-call chat was on fire again: the order service was healthy on every dashboard, but throughput had flatlined at ~800 req/s while P99 climbed past 4 seconds. The usual suspect? A thread pool sized by guesswork during a late-night deploy, six months earlier. We'd hand-tuned maxThreads to "something that felt right," and it wasn't right anymore. That's the moment I started appreciating a different default: in Solon, all of those knobs ship as 0 — meaning auto , derived from your machine's actual CPU cores at runtime. You can go months without thinking about a single thread-pool property. This post walks through the five knobs that exist, how the auto-tuning math works, and the three failure modes that tell you it's time to touch them. The five knobs under the hood Solon exposes these on app.yml (all values are the documented defaults): # Minimum threads for the http server (0 = auto; also accepts fixed values like 2, or core multiples like x2) server.http.coreThreads : 0 # Maximum threads for the http server (0 = auto; also accepts fixed values like 32, or core multiples like x32) server.http.maxThreads : 0 # Idle thread timeout in ms (0 = auto) # supported since v1.10.13 server.http.idleTimeout : 0 # Is this an IO-bound service? (default true) # supported since v1.12.2 server.http.ioBound : true # Enable the virtual thread pool (default false) # supported since v2.7.3 solon.threads.virtual.enabled : false Notice what's missing: no hard-coded defaults for coreThreads or maxThreads . 0 means "figure it out from the hardware." That single decision removes a whole class of "copy-pasted tuning values" problems — the ones that were right for someone else's 32-core box and wrong for your 2-core container. CPU-bound or IO-bound: the one question that matters The auto-tuner only needs you to answer one question: is your workload CPU-bound or IO-bound? CPU-bound : the work happens entirely in CPU and memory — think a "hello world" handler that returns a s

Alibaba officially launched Qwen3.8-Max on Monday, marking the debut of its most substantial artificial intelligence model. This new open-weight release aims at enterprise sectors, specifically targeting software engineering and complex reasoning. It represents a significant expansion of the company’s existing portfolio of digital tools for large-scale business operations. Technical architecture and performance benchmarks The Qwen3.8-Max model utilizes a mixture-of-experts (MoE) design, featuring a total of 2.4 trillion parameters. However, the system only activates approximately 95 billion of those parameters during any single inference cycle. This approach balances high-level processing power with the need for operational speed. Alibaba plans to make the open-weight versions of this technology available to the public through its cloud-based studio platform starting next week. Company representatives stated that this new architecture ranks among the most capable systems currently in existence. They position it as a direct competitor to the most advanced frontier models available globally. Internal data suggests the performance levels are trailing only the very top tier of experimental AI systems. This move signals a clear intent to capture market share from established western technology firms. Competitive testing and industry analysis To prove its capabilities, Alibaba released internal data comparing Qwen3.8-Max against top models from Anthropic and OpenAI. The tests focused heavily on coding benchmarks such as SWE-bench Pro. According to the company, their new model held its own against Claude Opus 4.8 and GPT-5.6 Sol. They utilized the specific coding frameworks recommended by each competitor to ensure a fair and rigorous comparison during the evaluation process. Industry analysts have noted that the gap between proprietary and open-weight models is closing rapidly. While proprietary leaders still hold certain advantages, the rise of open-weight alternatives pr

Somewhere on your feed right now, someone is bragging about the app they built in a weekend, no engineering background, no team, just a prompt and a Saturday. The post always ends the same way. Look what I built without needing any of you. Anyone can build software now. That is the whole pitch, repeated in a hundred different captions this month alone. Here is what that post never shows you. The part where someone checks it. Not "does it run." Checks it. Someone who did not write it, looking for the version of it that fails, the input nobody thought to try, the assumption that was wrong in a way the builder was structurally the worst person to catch, because they were too close to their own idea to see the hole in it. That someone is not optional. It is the actual job. None of this is theoretical. A notification icon that, instead of opening a panel, closes the entire page and drops me back on an empty tab. A video call that disconnects mid sentence for no visible reason. A video that plays with the sound simply gone, until I restart it. I do not have a chart proving reliability across the industry is getting worse. What I have is a pattern I keep running into, on products built by some of the most resourced engineering organizations on earth. Why software engineering has more than one person in the room A developer writes the code. A reviewer reads it before it merges. QA tries to break it on purpose. A manager decides if it is actually ready, or just finished. None of these roles exist because engineers do not trust themselves. They exist because a single person, however good, cannot see their own blind spots. That is not a flaw in the person. It is a fact about how blind spots work. Ten sets of eyes exist so that the eleventh mistake gets caught before a million people hit it. We have already watched what happens when that layer disappears, and we did not need AI to run the experiment. We ran it with the spreadsheet. The spreadsheet already showed us the cost of

Samsung says it's banning smart TV apps that expose users' internet connections.

PR #5386 adds a pure Codename One text-editing path. EditField , RichTextArea , and CodeEditor can now keep their document, selection, and painting inside the lightweight UI while each port supplies keyboard and input-method events. What is Codename One? Codename One is an open-source framework for building native iOS, Android, desktop, and web apps from a single Java or Kotlin codebase. Learn more at codenameone.com . Text input must handle virtual keyboards, hardware keys, autocorrect, dictation, marked text from an input method editor, bidirectional text, selection, clipboard formats, and accessibility geometry. Codename One traditionally delegates that work to a native platform field placed over the lightweight component during editing. The overlay remains the default for TextField and TextArea . It can create a small visual jump, and it cannot participate in lightweight painting for syntax highlights, rich runs, masks, inline images, or a custom selection model. The port sends text operations instead of key codes A soft keyboard does not type keys. It commits words, replaces a marked composition range, deletes text around the caret, and changes selection. Dictation may insert a sentence without producing one key event. The new TextInputClient contract models those operations: commitText(...) inserts final text. setComposingText(...) replaces the active marked-text range. finishComposing() accepts that range. deleteSurroundingText(...) implements virtual-keyboard deletion. onKeyCommand(...) carries navigation, selection, clipboard, undo, and redo. Geometry queries locate the caret and selection for candidate windows and accessibility. All offsets use UTF-16 indices. That matches Java String , Android Editable , and Apple string APIs. The document normalizes line endings before it updates selection, undo history, formatting runs, or the state returned to the platform. The port still owns the keyboard session. Codename One owns the document and what appears on scr

Deliberately best-effort — a settings tweak failing shouldn’t fail a deployment that already succeeded. It surfaces as public: false in the response and a visible warning in the UI, rather than silently handing someone a broken link. Live Stripe checkout failed on day one. Test mode worked perfectly. Live mode returned: Invalid line_items[0]: the product tax code is missing… Product tax code is required for Managed Payments, which is enabled by default on your account. A whole class of bug that only exists in production. I reproduced it directly against Stripe’s API before touching code, then opted the session out of Managed Payments — rather than inventing a tax classification, since whether to collect sales tax is a business decision, not a code one. The meta-lesson Every one of these five bugs was invisible to the test suite. The unit tests were green the entire time — because they tested my parsing logic, and every bug was in the query I sent or the transport I sent it over. Four of them were only findable by hitting the live endpoint and reading actual output. The 1996–2006 bug in particular looked like a total success from every angle except one: 566 results, HTTP 200, tests passing, correct shape. You had to actually look at the dates. Best Use of Sentry Not submitting to this category — DAREALTYTE doesn’t currently use Sentry. Worth being straight about it, since this project is a decent argument for adding it. Bugs 4 and 5 both returned HTTP 200 with well-formed payloads. Error monitoring wouldn’t have flagged either one; nothing threw. What would have caught them is exactly what I ended up doing by hand — inspecting real production responses and noticing the values were wrong even though the shape was right. The one place Sentry would have paid off immediately is Bug 3. The Safari Load failed was reported to me as a screenshot from a phone, with no stack trace and no way to reproduce it in my own environment. A Session Replay or a captured client-side exce

Cuando llevamos modelos de lenguaje o IA a producción, la latencia es nuestro principal enemigo. Evaluar un modelo únicamente por su precisión ignora un factor crítico: el rendimiento computacional. En este post analizamos por qué la velocidad (medida en tokens por segundo) se ha convertido en una métrica clave de arquitectura y cómo puedes empezar a medirla. ¿Por qué importa la velocidad? Reducción de Latencia: Aplicaciones críticas (finanzas, salud, automatizaciones) no pueden esperar segundos por una respuesta. Eficiencia de Recursos: Optimizar el rendimiento disminuye el uso prolongado de GPUs, reduciendo directamente la factura cloud. Técnicas Clave: El uso de arquitecturas ligeras, cuantización y batch processing permite mantener la precisión mientras se incrementa el rendimiento. Ejemplo Práctico: Midiendo el rendimiento en Python Un enfoque inicial para medir la tasa de procesamiento de datos/tokens en tus pruebas de rendimiento: import time def medir_velocidad ( modelo , datos ): inicio = time . time () # Procesamiento del conjunto de datos o tokens respuesta = modelo . procesar ( datos ) fin = time . time () tiempo_total = fin - inicio tokens_procesados = len ( datos ) # O conteo exacto de tokens generados/procesados velocidad = tokens_procesados / tiempo_total print ( f " Tiempo total: { tiempo_total : . 2 f } s " ) print ( f " Rendimiento: { velocidad : . 2 f } tokens/segundo " ) return velocidad Tip de Arquitectura: Un objetivo de ~100 tokens/seg es una excelente referencia para sistemas que requieren interacción humana en tiempo real. Pasos sugeridos para optimizar: Benchmark inicial: Establece tu baseline de tokens/seg. Batch Processing: Agrupa solicitudes para maximizar el paralelismo. Modelos Destilados/Cuantizados: Evalúa si un modelo más pequeño satisface el caso de uso con una fracción de la latencia. 💬 Comunidad Pivelcode: ¿Qué herramientas o librerías utilizas para hacer profiling y benchmarking de tus modelos de IA? ¡Déjalo en los comentarios!

AI agents are everywhere in 2026. Most of them can answer questions, generate code, or automate simple workflows. But once you ask them to interact with a real computer—browsers, desktop applications, terminals, files, and external services—things quickly become unreliable. That was the motivation behind HeyAgent . The Problem Most autonomous agents fail for one of three reasons: They declare success before the task is actually finished. They lose context during long, multi-step workflows. They aren't designed to work with a real desktop environment. I wanted to build an agent that behaves more like a real assistant instead of just another LLM wrapper. What HeyAgent Does HeyAgent is an open-source autonomous AI agent for computer control and workflow automation. It can: 🖥️ Control desktop applications 🌐 Work inside browsers 📂 Read and manage files 💻 Execute terminal commands 🔗 Connect with external services 📱 Be controlled through CLI, Desktop UI, or Telegram 🧠 Plan and execute multi-step workflows ✅ Verify results before marking tasks as completed Instead of blindly executing prompts, the agent plans, executes, validates the outcome, and only then reports success. Reducing False Task Completion One of the biggest problems I noticed in existing AI agents is false task completion. Many agents click a button, assume everything worked, and immediately report success. In reality, something may have failed several steps earlier. HeyAgent performs additional verification after critical actions to reduce false positives and improve reliability during long-running workflows. Built with AWS Support HeyAgent has been significantly accelerated thanks to the support of AWS. AWS has provided the project with cloud infrastructure, GPU computing resources, and access to modern AI services that made rapid experimentation possible throughout development. From running GPU workloads to evaluating different LLMs and AI models, AWS has been an important part of the engineering process.

I tried to do all my NeurIPS reviews responsibly, even for the papers I suspected to be AI slop. I even gave what apparently were very nice scores compared to the scores I ended up getting. (I don't just mean the absolute number for my scores were higher, but that they were calibrated differently--I only rejected for severe issues, while I had a reviewer who only raised very minor issues but gave a reject, with a 1 for all the subscores.) I got shockingly bad reviews for my own paper; two of them were straight up adversarial. (I have quite a bit of experience publishing at this point, so I say with some confidence that I rolled an unusually adversarial batch.) The AC was almost nonresponsive until the last day. All but one of the reviewers was nonresponsive, only one responded when the AC prompted them to, and that was to say that their concerns were addressed but they maintained their reject score. I'm not surprised by my experience given how much of a lottery these conferences are, but it's a very toxic system. submitted by /u/WhiteBear2018 [link] [留言]

So our paper had very good initial reviews but one of the reviewers decreased now their score although we addressed 3 out of 4 weaknesses. There’s no further justification or something like “your results arise more issues”. It seems to be very annoying because why decreasing now and not having assigned the lower score beforehand. I wanted to ask to people that was accepted previously with “middle” scores from reviewers (avg 3.5 for example), because I guess that in those cases AC helped to push up the scores. Did you focus more on the meta review? Was your AC talkative with you, or forcing the reviewers to engage? Our AC has been silent since the meta review but I guess that maybe they are busy with other papers submitted by /u/pdastronut [link] [留言]

Potentially a hot take? I am not sure why our community is plagued with reviewers who, after acknowledging that their concerns were addressed by a rebuttal, decide to maintain their score because they don't vibe with the paper. So here is my plea to all reviewers: If you list a set of concerns in your review and these concerns are addressed during the rebuttal, please adjust your score accordingly. This should apply whether or not you like the paper and/or its methodology. The beauty of scientific research is that we each get to explore ideas that we find meaningful whose value may not be immediately obvious to every individual reviewer. submitted by /u/undesirable_12 [link] [留言]

Today, I’m talking with Toni Schneider, who is the brand new CEO of the social platform Bluesky — he formally took over after a short stint as interim CEO. This is one of my favorite kinds of interviews to do on Decoder, because a couple years ago, we had Bluesky’s prior CEO, Jay Graber, on […]

After its 1.0 launch last month, Palworld is coming to iOS and Android with a new open-world MMORPG launching later this year, Polygon reports. Garena, the developer behind the new game, says in its announcement that Palworld Online "reimagines the depth, freedom, and scale of the original PC survival creature-collecting title for mobile." The core […]

There are several steps you can take to prevent your phone from getting dangerously hot.

This week's Java roundup for July 27th, 2026, features news highlighting: OpenJDK JEPs targeted and proposed to target for JDK 28; the GA release of GPULlama3.java 1.0; point releases of Micronaut, Quarkus and JobRunr; a maintenance release of JDKUpdater; the sixth release candidate of Maven 4.0; and the first milestone release of Jakarta Agentic AI 1.0. By Michael Redlich

Spider-Man: Brand New Day is joining The Odyssey in IMAX theaters after both movies led the biggest weekend in box office history. On Monday, IMAX announced that Spider-Man: Brand New Day will be available in select digital IMAX locations across North America starting Thursday, August 6th. The news comes as The Odyssey's exclusive three-weekend run […]

HubSpot has redesigned its Just-In-Time Access (JITA) authorization system using a rule engine architecture. The system evaluates access requests through independent rules organized as a directed acyclic graph, adding structured decision metadata, rule-level observability, and governance workflows to replace complex conditional authorization logic. By Leela Kumili

The Galaxy Z Fold 8 Ultra's new processor, brighter screens and chassis tweaks are nice, but the updates don't do enough to earn the 'ultra' tag.