标签：#AI

Google has connected Gemini Live with its Deep Research capability, allowing users to begin a multi-step research task by voice, leave it running in the background, and return for a spoken or transcript-based follow-up when the work is complete. The change turns Deep Research from a primarily prompt-led activity into a more conversational mobile workflow, particularly for people who need to capture a research request without staying in the app. The key distinction is not simply voice input. Gemini Live can initiate a research process that continues while a user switches apps or locks their phone. Google describes the resulting experience as a way to talk through research, with a notification when the task has finished and a seamless path back into conversation. The company's Gemini Deep Research overview for Pixel presents the capability as part of a broader effort to make in-depth research more usable on mobile devices. Deep Research itself is designed to do more than provide a single response. Google has documented a workflow in which Gemini develops a research plan, searches across sources, expands its investigation as needed, and produces a structured report with links to sources. Reports can also be exported to Google Docs. Bringing that process into Gemini Live changes how a request can begin and how a user can resume it, rather than changing the documented purpose of Deep Research. What changes in the Gemini Live research workflow The update combines conversational initiation with asynchronous execution. A user can explain a complex topic aloud, ask Gemini Live to begin Deep Research, and move on to another task while the system works. When the report is ready, the user can be notified and continue through speech or review the transcript. Workflow element Documented Deep Research experience Gemini Live integration Starting a request A research request can lead to a structured plan. A user can initiate Deep Research by speaking with Gemini Live. Research proce

Google is expanding Gemini into a more structured learning environment with study notebooks , a student-focused workspace for diagnostics, personalized lessons, practice quizzes, flashcards and progress tracking. The rollout turns Gemini from a general-purpose assistant into a tool designed to organize source-based study workflows, beginning with web access worldwide and mobile support planned for later in the summer. In Google's official study notebooks announcement , the company describes a workflow that starts by assessing a learner's baseline knowledge. Gemini can then create smaller lessons tailored to a student's goals and reinforce those lessons with quizzes. The company positions the capability as part of a broader education-focused effort across Gemini and NotebookLM, rather than solely as a standalone product called Student Hub. What Gemini study notebooks add The central change is a dedicated notebook space where students can bring together their course materials and ask Gemini to produce learning activities from them. Google says users can upload sources including notes, PDFs and websites, then generate flashcards and quizzes inside a notebook. Study notebooks can also reference uploaded materials and sources while creating lessons. This structure matters because it moves the interaction beyond one-off prompts. A diagnostic quiz establishes a starting point, personalized bite-sized lessons address a learning goal, and practice quizzes provide a way to revisit material. A dashboard tracks progress within that workflow. Google also points to connections with NotebookLM, including the ability to reference past chats and outputs there. Study notebook element Confirmed role in the workflow Availability described by Google Diagnostic quizzes Establish a learner's baseline knowledge Part of the study-notebook experience Personalized lessons Create bite-sized learning content tailored to goals Part of the study-notebook experience Flashcards and practice quizzes

Agent Skills turn repeated prompts into reusable, inspectable workflows. This collection includes five small skills for work that comes up often when building with OpenCode or Claude Code: reviewing public copy, checking text limits, capturing public webpages as PDFs, sending task notifications, and structuring research for later use. The full index is available at Published Agent Skills . For OpenCode, Skills can live in ~/.config/opencode/skills/ or project-level locations supported by your setup. Claude Code can discover Skills from .claude/skills/ . Put a Skill folder in the right location, restart the agent session if needed, and it becomes available when the task matches its description. 1. AI Writing Detector Skill AI Writing Detector Skill reviews English and Brazilian Portuguese copy for patterns that make AI-written text feel generic. It checks common issues such as repeated sentence rhythm, filler phrases, excessive formatting, and em-dash use. It also ships with a CLI and MCP server for text and file linting. Useful prompts: Review this README introduction with the anti-ai-tells Skill. Keep the technical facts, flag generic wording, and suggest direct replacements. Run the writing linter on this release post, then rewrite only the passages that need attention. This is useful before publishing documentation, launch posts, landing pages, and changelogs. 2. Text Counter Skill Text Counter Skill gives exact counts for characters, words, sentences, paragraphs, lines, graphemes, bytes, and phrase occurrences. It helps when "roughly under the limit" is not enough. Useful prompts: Write a 155-character meta description for this package and verify its exact character count. Reduce this GitHub issue title to 80 characters without removing the error code. Count the phrase "OpenCode" in this Markdown file. The Skill makes counting rules explicit. That avoids surprises with spaces, Unicode characters, emoji, or repeated phrases. 3. HTML to PDF Skill HTML to PDF Skill

OpenAI is positioning Zero Data Retention (ZDR) as a scalable privacy control for eligible frontier-model API and enterprise workloads. The policy matters as businesses use more capable models for longer-running and increasingly autonomous work, where prompts, outputs, and related interactions can contain sensitive operational, customer, or proprietary information. On its official API platform page , OpenAI lists "Zero data retention policy by request" alongside access to frontier models and APIs. The company’s enterprise privacy materials and GPT-5.4 release information add important context: ZDR is a configurable option for eligible organizations and endpoints, rather than a universal default across all OpenAI services or customer configurations. The shift is less about a newly invented privacy principle than about applying retention controls more explicitly to frontier-capable deployments. OpenAI’s GPT-5.4 materials describe Zero Data Retention surfaces and safety controls designed for higher-sensitivity contexts. That framing acknowledges a practical tension for enterprise AI: more autonomous systems can create more valuable workflows, but they also require safety systems that assess risks across related interactions. What Zero Data Retention changes for enterprise AI Under ZDR, OpenAI disables logging of customer content for abuse monitoring and model-training purposes. The setting also affects API behavior. For example, the store parameter for chat completions and responses is forced to false in ZDR contexts. That is a meaningful control for teams that need to minimize the persistence of prompt and response content. It should not, however, be interpreted as a blanket statement that no information can ever be retained anywhere in the service. OpenAI documents that some endpoints may retain application state or metadata for operational reasons. It also describes exceptional safety and retention mechanisms, including Eyes Off and Safety Retention , that may apply

Bankrupt Spirit accused of selling out workers in massive data sale to Google.

The actress' firm Kinship Ventures is an investor in the company.

In order to beef up its ability to accurately measure viewership data in the streaming era, Nielsen is moving forward with a plan to use more information gathered from its partners' wearable devices. Today, Nielsen announced "a number of key enhancements" that are being added to its data capturing process ahead of the upcoming fall […]

During a recent visit to Generalist AI, I watched a robotic arm improvise and use a banana as a tool.

As AI becomes harder to avoid, consumers are growing more wary of the technology — and Silicon Valley is discovering that widespread adoption doesn’t necessarily lead to acceptance.

College students can get a free year of Google's AI Pro plan, something that would normally cost $200.

The launch of the new study features marks Google's latest effort to make Gemini the AI assistant that students turn to when learning and studying, as it continues to compete with companies like OpenAI.

As we're gearing up for back-to-school season, Google is rolling out a new dedicated student hub in Gemini. It's a one-stop repository for collecting research in a study notebook, creating flashcards, taking practice quizzes, and more. Google is also enhancing its study notebooks with support for graphs and images. It can even add test dates […]

Multiple cybersecurity researchers said they suddenly lost access to OpenAI’s Trusted Access for Cyber (TAC) program, which offers models with fewer guardrails for vetted users.

You picked a free model because the answers looked good. Good answers are not an endpoint. An endpoint is the model plus the server plus the network. Demos pass. Pipelines stall. The model was rarely the problem. So why do we keep benchmarking only the model? Because it is easy. You paste a prompt. You read the output. You declare a winner. The server never gets a vote. This post is a reproducible benchmark. It measures the pair, not the model. Run it before you wire any free endpoint into CI. The Pair, Not the Model Most evaluations compare answers. You paste a prompt. You judge the output. You pick a winner. That measures the model. It ignores the server. Free model access usually means a shared endpoint. A free server option means shared tenancy. Other users share the CPU, memory, and network. Your latency is their latency. Your timeout is their timeout. Here is the scenario I keep seeing. A team evaluates a free model on Friday. The answers look great. They wire it into CI on Monday. By Wednesday, the pipeline is red. The model did not change. The server did. A neighbor started a batch job. Now every request queues behind it. I applied the same harness to MonkeyCode's free model access and their free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I did not trust the demo. I built a harness instead. The Harness A benchmark needs three things. A fixed prompt set. A concurrency ladder. A pass/fail table. Here is the harness I use. #!/usr/bin/env python3 """ Benchmark a model endpoint as a pair: model + server. """ import argparse import asyncio import json import statistics import time import httpx PROMPTS = [ " Say OK. " , " Classify this log line: ERROR disk full " , " Return one word: is 429 a retryable status? " , ] async def fire ( client , url , payload , sem , timeout = 30 ): async with sem : start = time . perf_counter () try : r = await client . post ( url , json = payload , timeout = timeout ) return r . sta

A rollback plan tells you how to undo an AI change, but not what breaks first when the change stays in place. Most production incidents do not begin with a deliberate rollback; they begin with an unexpected failure mode that the author never tested. I now treat a passing fault drill as a precondition for reviewing any AI-generated server patch. The drill runs on a disposable server before a human reads a single line of the diff. Why a rollback plan is not enough A rollback plan answers a question about the past: how do we return the system to a known state? A fault drill answers a question about the future: what happens when this change meets a condition the author did not imagine? The second question decides whether you get paged at 3 a.m. A change with a perfect rollback can still fail in a way that nobody notices until the data is gone. Free model access changes the economics of this argument, because generation stops being the bottleneck and verification starts. When a draft is nearly free, the cheapest verification is the one that breaks the change on purpose. A rollback plan is documentation; a fault drill is evidence. Documentation tells you what should happen, while evidence tells you what actually happens on a real service manager. The fault drill in five steps The workflow assumes two cheap resources: a model that generates failure hypotheses from a diff, and a server that can be destroyed after the drill. MonkeyCode's free model access covers the first, and its free server option covers the second, so a drill costs almost nothing. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Any ephemeral VM or container host works if you prefer a different provider. 1. Generate failure modes before you apply anything Ask the model to enumerate failure modes for the diff, and forbid it from proposing fixes, because fixes are a distraction at this stage. The prompt below is the one I use, and it produces a catalog that the drill can test.

Conclusion first: a free model drafted a working C++17 directory hasher in one pass. The draft compiled, ran, and was still wrong. A differential test against standard system tools found three real bugs before the tool ever touched a production cache. Generation was the cheap part. Verification was the deliverable. Background I needed a deterministic hash of a directory tree. The use case was cache invalidation for a small build pipeline: if any file content, name, or symlink target changes, the cache key must change. If nothing changes, the key must stay identical across machines and across checkouts. Hand-writing the tool is maybe 200 lines of std::filesystem code. The happy path is easy. The risk lives in ordering, symlinks, and metadata leaking into the hash. I turned the task into an experiment. MonkeyCode's free model access and free server option meant the model ran on a remote server while I kept verification on my laptop. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The plan: let the model write the first version, then prove or disprove it against a reference oracle. The Contract The goal was not "a tool that compiles." The goal was a tool that matches a reference implementation on every input I could generate. I wrote the contract in three sentences: Same tree → same hash, on any machine. Different content, name, or symlink target → different hash. File metadata (mtime, inode) must not affect the hash. Implementation Step 1: the prompt. I gave the model the contract, the C++17 standard, and one constraint: a single file with no dependencies beyond the standard library. Step 2: the draft. The model returned one .cpp file in a single response. It compiled on the first try. That is the exact moment where most workflows stop. This one did not. Step 3: the reference oracle. Instead of reviewing the code line by line, I built a harness that compares the tool against a shell pipeline: find " $tree " -printf '%P\0' | sort -z | wh

MCP x-mcp-header validation is easy to miss because the annotation looks like ordinary JSON Schema metadata. On the 2026-07-28 Streamable HTTP transport, it is a wire contract: the client copies selected tool arguments into Mcp-Param-* headers, intermediaries can act on those headers, and the server checks them against the JSON-RPC body. I treat that contract as something to test before a tool reaches tools/list . A bad suffix, an unsupported type, or an unreachable annotation makes the whole tool definition invalid. Silently accepting it only moves the failure to a harder place to diagnose. Why the same value travels twice The final Streamable HTTP specification mirrors request metadata into HTTP headers so a load balancer, gateway, or WAF does not need to parse JSON-RPC. A server can add x-mcp-header to a tool property: { "type" : "object" , "properties" : { "region" : { "type" : "string" , "x-mcp-header" : "Region" } } } A call with "region": "us-west1" then carries: Mcp-Param-Region: us-west1 The official C# SDK can generate that schema from a parameter attribute: [ McpServerTool ] public static string ExecuteSql ( [ McpHeader ( "Region" )] string region , string query ) => $"Queued for { region } " ; Current C# SDK v2 tool documentation describes both schema generation and automatic header projection. The feature is on the stable v2 line; it is not necessary to pin an earlier preview or release candidate. MCP x-mcp-header validation rules The final tool definition rules are deliberately narrow. The annotation value must be a non-empty HTTP field-name token and must be unique without regard to case. Region and region therefore collide. Control characters, spaces, and separators such as a colon are not valid suffix characters. Only string , integer , and boolean properties can be mirrored. JSON Schema number is excluded, and integer values must stay between -(2^53 - 1) and 2^53 - 1 so every conforming implementation can represent the value exactly. Reachability i

Valve just leaked several new videos showing the Steam Frame's unboxing, setup process, and various accessories. The videos apparently appeared on the ARM Steam client following an update, but they were "quickly pulled after that," according to the Steam Hardware Updates account on X. The Steam Frame is Valve's virtual-reality headset that can stream games […]

If you’re juggling multiple Copilot sessions, use the My work pane to track what's in flight, what's done, and what's next. The post GitHub Copilot app for Beginners: Managing your work appeared first on The GitHub Blog .

The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or for firms to hedge their exposure when the price changes. Silicon Data […]