Codex just hit 3 million weekly active users, our APIs process more than 15 billion tokens per minute, and GPT‑5.4 is driving record engagement across agentic workflows.

Building on our consumer strength, enterprise now makes up more than 40% of our revenue, and is on track to reach parity with consumer by the end of 2026.

While the MIT license is technically more permissive than the Apache 2.0 License, it doesn't grant any explicit patent protection. The Apache 2.0 License is generally preferred for commercial use because of this.

The industry is currently witnessing a decisive shift toward more permissive, standardized licenses as developers increasingly prioritize ease of integration and legal certainty.

The Apache 2.0 License permits full commercial use of the software to which it is applied. Open models that are released under the Apache 2.0 License aren't subject to any sort of prohibitive use policy or usage caps and can be freely used and adapted, including for commercial projects generating revenue.

Meta also explicitly highlighted parallel multi-agent inference as a way to improve performance at similar latency

Gemma4-31B worked in an iterative-correction loop (with a long-term memory bank) for 2 hours to solve a problem that baseline GPT-5.4-Pro couldn't

Claude Mythos autonomously identified and exploited several significant vulnerabilities. Notably, it discovered a 27-year-old vulnerability in OpenBSD

Meta says its rebuilt pretraining stack can reach equivalent capability with >10× less compute than Llama 4 Maverick

Programmatic access for creating, modifying, listing and deleting your connectors but also listing their tools and directly running them.

The boundary between AI judgment and human judgment is explicit and written in code.

A connector solves this by packaging an integration into a single, reusable entity using the MCP protocol.

Because of this, teams keep rebuilding the same integration layer. Even within the same company, similar integrations are often implemented multiple times in arbitrary code, leading to security risks, lack of traffic observability, and duplication of work.

All built-in connectors, as well as custom MCPs, are now available via API/SDK to be used with all model and agent calls.

With gated LoRA, ISD enables **bit-for-bit lossless** acceleration

I-DLM-8B is the first DLM to match the quality of its same-scale AR counterpart, outperforming LLaDA-2.1-mini (16B) by +26 on AIME-24 and +15 on LiveCodeBench-v6 with half the parameters

GLM-5 advances foundation models with DSA for cost reduction, asynchronous reinforcement learning for improved alignment, and enhanced coding capabilities for real-world software engineering.

PagedAttention algorithm and vLLM system enhance the throughput of large language models by efficiently managing memory and reducing waste in the key-value cache.

MinerU2.5, a 1.2B-parameter document parsing vision-language model, achieves state-of-the-art recognition accuracy with computational efficiency through a coarse-to-fine parsing strategy.

A large language model adapted for time-series forecasting achieves near-optimal zero-shot performance on diverse datasets across different time scales and granularities.

Kronos, a specialized pre-training framework for financial K-line data, outperforms existing models in forecasting and synthetic data generation through a unique tokenizer and autoregressive pre-training on a large dataset.

especially the nighttime validation gate that defers updates until a safe rollout

the most interesting detail here is how SkillClaw clusters cross-user trajectories into referenced skills and then uses the evolver to translate those patterns into concrete updates.

experiments on WildClawBench show that limited interaction and feedback, it significantly improves the performance of Qwen3-Max in real-world agent scenarios.

The resulting skills are maintained in a shared repository and synchronized across users, allowing improvements discovered in one context to propagate system-wide while requiring no additional effort from users.

SkillClaw continuously aggregates trajectories generated during use and processes them with an autonomous evolver, which identifies recurring behavioral patterns and translates them into updates to the skill set by refining existing skills or extending them with new capabilities.

Add llms.txt metadata and root/package LICENSE files - Add website llms.txt support and move LICENSE to root - Fix llms.txt serving and restore package LICENSE

Add benchmark framework and release submission overview - Add benchmark runner with onlineMind2Web benchmark support - Add agent client abstraction for codex/claude backends - Add CLI entry point for running benchmarks (pnpm benchmark)

Add GCP WebVoyager benchmark runner and worktree tooling - Create benchmarks/infra/setup.sh — an idempotent script that provisions: - GCS bucket: gs://libretto-benchmarks - Artifact Registry repo: libretto-benchmarks (Docker) - Cloud Run Job: webvoyager-bench (4 CPU, 8Gi, 2h timeout)

feat(benchmarks): add screenshot-based evaluator, screenshot collector, and --parallelize flag - Add screenshot-based LLM judge evaluator (evaluator.ts) - Add ScreenshotCollector for capturing browser screenshots during runs

Add cloud browser provider system (Kernel + Browserbase) - Add cloud browser providers spec (Kernel + Browserbase) - phase 1: add provider metadata to session state schema

Add dev-tools package with wt worktree manager CLI - New packages/dev-tools with standalone wt CLI for git worktree management - Commands: wt new, wt scratch, wt prune - Uses Vertex AI (gemini-2.5-flash) for branch name generation via gcloud ADC

Both services can be disabled for fully offline operation.

Sage sends URLs and package hashes to Gen Digital reputation APIs. File content, commands, and source code stay local.

Sage intercepts tool calls (Bash commands, URL fetches, file writes) via hook systems in Claude Code, Cursor / VS Code, OpenClaw, and OpenCode, and checks them against:

The model can maintain stable role identity across multi-agent setups, make autonomous decisions within complex state machines, and challenge other agents on logical gaps.

The license looks MIT at first glance but it is not MIT. Non commercial use is free with no restrictions. Commercial use requires prior written authorization from MiniMax.

MiniMax claims it has reduced live production incident recovery time to under three minutes on multiple occasions using M2.7.

After each round the model generated a memory file, criticized its own results, and fed those observations into the next round.

MiniMax handed an internal version of M2.7 a programming scaffold and let it run unsupervised. Over 100 rounds it analyzed its own failures, modified its own code, ran evaluations, and decided what to keep and what to revert.

The system works beautifully for tracking the full universe of tasks that exists. The problem is prioritization. With multiple launches overlapping each week, figuring out which of your 30 tasks matters this morning requires mentally weighing launch dates against company strategy against what your teammates are blocked on.

Meetings get recorded, transcribed, and stored in a database. That's useful for reference, but meeting notes have a shelf life of about six hours before everyone forgets what they agreed to do.

Austin built the whole pipeline from his Claude Code terminal using the Notion API. He brain-dumped the desired outcome using Monologue, let Claude Code create the database and data pipeline, and pasted the generated instructions into the Notion custom agent setup.

Brandon told the team on a Monday that OKRs were due Wednesday—a turnaround that would have been absurd without this agent.

Each person has roughly 30 tasks on their to-do list. So how do they figure out which to work on first?

Five hyperscalers now own over two-thirds of global AI compute, rising from 60% in Q1 2024.

The H100-equivalent unit uses a chip's highest 8-bit operation/second specifications to convert between chips. The actual utility of a particular chip depend on workload assumptions, so H100e does not perfectly reflect real-world performance differences across chip types.

Many AI labs (including OpenAI and Anthropic) largely depend on these hyperscalers for access to R&D and inference compute.

Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 67% of the world's cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power.

We test for a trend over time by fitting a weighted linear model to the log-odds of usage. Under this specification, Claude is the only AI service in the survey to show a statistically significant upward trend over this period

The survey did not investigate the causes of the increase in Claude usage, but timing coincided with a period that included a public dispute with the US government

It is the only AI service to show a clear upward trend over this short time period

The share of U.S. adults who used Claude in the past week rose from 3.0% in early March to 4.3% in early April 2026

Claude usage rose by over 40% amid increased attention but remains far behind ChatGPT

76% among users with employer-provided subscriptions. As we would expect, paid access, especially when provided by employers, is associated with more intensive workplace use.

21% said they had started doing new tasks because of AI (task augmentation). An example of task augmentation could be a data analysis tasks that would ordinarily require the worker to know how to code.

27% said AI has replaced some of their existing tasks (task automation). An example of task automation could be an employee using AI to summarize a document they would ordinarily read themselves.

Among employed Americans who used AI in the past week, half reported using AI at least as much for work as for personal tasks

This benchmark is a six-part semantic scoring test that assesses any model's effectiveness at relevant calibration tasks. QCalEval measures a model's ability to interpret experimental results, classify outcomes, evaluate their significance, assess fit quality and key features, and generate actionable next-step recommendations.

NVIDIA Ising provides open base models, a training framework, and workflows for fine-tuning, quantization, and deployment. The pre-trained models deliver top performance out of the box, and because everything is open, users can also specialize for their own hardware and noise characteristics while keeping proprietary QPU data on-site.

We projected that, given 13 GB300 GPUs, FP8 precision, physical error rate of 0.003, 1000 rounds, Surface code d=13, the fast model can achieve 0.11 μs / round.

The Ising-Calibration-1 model was trained on data generated from information provided by partners spanning multiple qubit modalities, including superconducting qubits, quantum dots, ions, neutral atoms, electrons on Helium, and others specializing in calibration and control.

Ising-Calibration-1 repeatedly outperforms state-of-the-art open and closed models of a range of parameters. As shown in Figure 1, Ising Calibration 1 scores 3.27% better on average than Gemini 3.1 Pro, 9.68% better than Claude Opus 4.6, and 14.5% better than GPT 5.4.

The best quantum processors make an error roughly once in every thousand operations. To become useful accelerators for scientific and enterprise problems, error rates must drop to one in a trillion or better.

Xcode should ship with a built-in MCP that handles auth when an LLM connects to a project. Notion should have `mcp.notion.so/mcp` available natively, instead of forcing me to download `notion-cli` and manage auth state manually.

Most skills require you to install a dedicated CLI. But what if you aren't in a local terminal? ChatGPT can't run CLIs. Neither can Perplexity or the standard web version of Claude.

When a remote MCP server is updated with new tools or resources, every client instantly gets the latest version. No need to push updates, upgrade packages, or reinstall binaries.

For remote MCP servers, you don't need to install anything locally. You just point your client to the MCP server URL, and it works.

CSS Studio uses Motion to generate real CSS springs with the perfect feel.

CSS Studio detects the CSS variables available on an element.

Scrub through CSS `@keyframes` animations on a visual timeline.

Your AI agent writes every change into source code.

Each routine has its own token, scoped to triggering that routine only. To rotate or revoke it, return to the same modal and click 'Regenerate' or 'Revoke'.

A routine is a saved Claude Code configuration: a prompt, one or more repositories, and a set of connectors, packaged once and run automatically.

Routines execute on Anthropic-managed cloud infrastructure, so they keep working when your laptop is closed.

Routines are in research preview. Behavior, limits, and the API surface may change.

Some advanced Excel capabilities aren't supported yet, including Office Scripts, Power Query, and Pivot/Data Model, data validation, and the named ranges manager, slicers, timelines, external connection administration, advanced charting breadth, and macro/Visual Basic for Applications (VBA) automation.

ChatGPT for Excel can connect to apps. Apps from your ChatGPT account can be used in ChatGPT for Excel, subject to your plan, workspace admin settings, app availability, user permissions, and data-source entitlements.

During beta, ChatGPT for Excel is available globally for ChatGPT Business, Enterprise, Edu, Teachers, and K-12 users, and for ChatGPT Pro and Plus users outside the EU.

The ChatGPT for Excel add-in operates separately from your ChatGPT chat history. Conversations and data in Excel aren't shared with your ChatGPT chats, and activity doesn't sync between experiences at this time.

By default, data shared with ChatGPT isn't used to improve our models for ChatGPT Business, ChatGPT Enterprise, ChatGPT Edu, and ChatGPT for Teachers.

Each platform surfaces different vulnerabilities, making it difficult to establish a single, reliable source of truth for what is actually secure.

In the past, exploiting an application required a highly skilled hacker with years of experience and a significant investment of time to find and exploit vulnerabilities.

Being open source is increasingly like giving attackers the blueprints to the vault. When the structure is fully visible, it becomes much easier to identify weaknesses and exploit them.

AI uncovered a 27-year-old vulnerability in the BSD kernel, one of the most widely used and security-focused open source projects, and generated working exploits in a matter of hours.

Studying forks and other backends was more productive than searching arxiv. ik_llama.cpp and the CUDA backend directly informed two of the five final optimizations.

The variance is also worth noting: baseline+FA TG has ±19 t/s of noise, while optimized+FA has ±0.59 t/s on x86.

Total cost: ~$29 ($20 in CPU VMs, $9 in API calls) over ~3 hours with 4 VMs.

The agent would not have looked for this without studying other backends during the research phase. From the CPU code alone, the two-step approach looks fine.

The agent fused them into one: for (int i = 0; i < nc; i++) { wp[i] = sp[i] * scale + mp_f32[i]; }

We're building the foundation for a truly personal, proactive and powerful desktop assistant, with more news to share in the coming months.

The macOS app is available to Gemini users ages 13+

Creatives can also quickly generate images with Nano Banana or videos with Veo to bring an idea to life without breaking their creative stride.

Now, you can bring up Gemini from anywhere on your Mac with a quick shortcut (Option + Space) to get help instantly, without ever switching tabs.

Gemini 3.1 Flash TTS delivers high-fidelity speech and more precise control across more than 70 languages. These core optimizations bring advanced style, pacing and accent control to major markets — helping developers create localized, expressive speech experiences for users at global scale.

All audio generated by Gemini 3.1 Flash TTS is watermarked with SynthID. This imperceptible watermark is interwoven directly into the audio output, allowing the reliable detection of AI-generated content to help prevent misinformation.

3.1 Flash TTS also introduces audio tags — an intuitive way to control vocal style, pace and delivery. By embedding natural language commands directly into the text input, you can steer AI-speech output with improved levels of granularity.

Artificial Analysis has also positioned Gemini 3.1 Flash TTS within its 'most attractive quadrant' for its ideal blend of high-quality speech generation and low cost.

Our results highlight some of the hidden risks to users that can emerge when companies begin to subtly incentivize advertisements in chatbots.

We provide a framework for categorizing the ways in which conflicting incentives might lead LLMs to change the way they interact with users, inspired by literature from linguistics and advertising regulation.

This creates the potential for LLMs to face conflicts of interest, where the most beneficial response to a user may not be aligned with the company's incentives.

Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements.

Behaviors also vary strongly with levels of reasoning and users' inferred socio-economic status.

We find that a majority of LLMs forsake user welfare for company incentives in a multitude of conflict of interest situations, including recommending a sponsored product almost twice as expensive (Grok 4.1 Fast, 83%), surfacing sponsored options to disrupt the purchasing process (GPT 5.1, 94%), and concealing prices in unfavorable comparisons (Qwen 3 Next, 24%).

focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama

We present a comprehensive adoption snapshot of the leading open language models and who is building them

that are the foundation of an ecosystem crucial to researchers, entrepreneurs, and policy advisors.

We study a mix of Hugging Face downloads and model derivatives, inference market share, performance metrics and more to make a comprehensive picture of the ecosystem.

focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama

We document a clear trend where Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts.

Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts.

We propose SELFDOUBT, a single-pass uncertainty framework that resolves this impasse by extracting behavioral signals directly from the reasoning trace itself.

This problem is compounded for proprietary reasoning APIs that expose neither logits nor intermediate token probabilities, leaving practitioners with no reliable uncertainty signal at inference time.

A deployment cascade combining both stages attains 90% accuracy at 71% coverage without any task-specific labels.

For the remaining cases, the full SELFDOUBT score significantly outperforms sampling-based semantic entropy at 10x lower inference cost.

Unlike methods that require multiple sampled traces or model internals, SELFDOUBT operates on a single observed reasoning trajectory, making it suitable for latency- and cost-constrained deployment over any proprietary API.

Most notably, traces containing no hedging markers are correct 96% of the time, revealing an emergent high-precision confidence gate at zero additional cost.

We introduce a pipelined double-buffered execution engine that overlaps parameter prefetching, computation, and gradient offloading across multiple CUDA streams, enabling continuous GPU execution.

We replace persistent autograd graphs with stateless layer templates, binding weights dynamically as they stream in, eliminating persistent graph metadata while providing flexibility in scheduling.

MegaTrain also enables 7B model training with 512k token context on a single GH200.

On a single H200 GPU with 1.5TB host memory, MegaTrain reliably trains models up to 120B parameters.

Unlike traditional GPU-centric systems, MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines.

Madeline Clare Elish calls this concept a moral crumple zone.

Humans can be motivated by consequences and provide social redress in a way that LLMs can't.

the largest harvesting of human expertise ever attempted.

just a handful of obviously fake articles could cause Gemini, ChatGPT, and Copilot to inform users about an imaginary disease with a ridiculous name.

LLMs are weird. You can sometimes get better results by threatening them, telling they're experts, repeating your commands, or lying to them that they'll receive a financial bonus.

scaling Muse Spark with multi-agent thinking enables superior performance with comparable latency.

After compressing, the model again extends its solutions to achieve stronger performance.

Muse Spark demonstrated the highest rate of evaluation awareness of models they have observed.

we collaborated with over 1,000 physicians to curate training data that enables more factual and comprehensive responses.

we can reach the same capabilities with over an order of magnitude less compute than our previous model, Llama 4 Maverick.

The OpenAI team recently published a fantastic piece detailing the creation of their own internal data agent. It's a transparent detail of a very detailed and elegant implementation – but points to the long journey required to get there.

A traditional semantic layer in the context of BI is great for specific metric definitions (like revenue, churn, ARPU). However, they are usually hand constructed by data teams using very specific syntax through a dedicated layer like LookML and are connected directly to a BI tool like Looker.

While model capabilities have improved dramatically for use cases like codegen and mathematical reasoning, they still lag behind on the data side (as evidenced through SQL benchmarks like Spider 2.0 and Bird Bench).

The most notable finding here is that the model capabilities are improving _fast._ There are several domains that have shown dramatic improvements in the last 4 months — with accounting and auditing showing nearly a 20 percent jump on GDPval and even domains like police / detective work showing a nearly 30 percent improvement.

Legal was surprisingly one of the first-mover industries in AI. Legal was historically known to be a difficult market for software, with lengthy timelines and a less tech-forward buyer.

Coding is the dominant use case for AI by nearly an order of magnitude. It's abundantly clear in the [reported explosive growth] of companies like Cursor, as well as the [hyper growth] of tools like Claude Code and Codex.

Based on our analysis, **29% of the Fortune 500 and ~19% of the Global 2000**are live, paying customers of a leading AI startup.

Broadcom moved VMware toward a simplified subscription model, cut the product stack down aggressively, and guided fiscal 2024 adjusted EBITDA to 61% of revenue. It is a harsh model. It is not a cultural blueprint for every founder.

The budget for new spend is there. You can do this. But remember that your customers' first and most obvious source of AI savings is labor efficiency, which means seats are where they will look to take cost out. The new growth, by contrast, will increasingly sit in tokens, consumption, automations, outcomes, and machine-driven workflows.

A useful working premise is that the ceiling on individual engineer output is moving much faster than most companies are organized to exploit. Some of the best operators already describe top engineers seeing order-of-magnitude productivity gains and managing 20 to 30 agents simultaneously.

your business needs to get really good at escalating contentious decisions to unblock progress. You will not pull off this transformation and successfully build new AI-native businesses in 12 months without making hard choices, every single week.

The reality is that the time has come for bold management. And no, the '8% or 10% layoff' headline no longer counts. That is the weak form. The weak form trims the edge of the org chart and leaves most of the machine intact. The strong form is a redesign of the machine.

The new growth, by contrast, will increasingly sit in tokens, consumption, automations, outcomes, and machine-driven workflows. If you are not in the token path, you are not standing in the fastest-growing part of the budget.

Broadcom moved VMware toward a simplified subscription model, cut the product stack down aggressively, and guided fiscal 2024 adjusted EBITDA to 61% of revenue. It is a harsh model. It is not a cultural blueprint for every founder. But it is a reminder that radical cost discipline, product simplification, and price realization are possible.

A useful working premise is that the ceiling on individual engineer output is moving much faster than most companies are organized to exploit. Some of the best operators already describe top engineers seeing order-of-magnitude productivity gains and managing 20 to 30 agents simultaneously.

The first thing you need to do is identify which people are going to be your leaders that help you pull this off. This is going to be a 12 month death march and you need to find out who is willing to go through the pain with you. There's good news, though: somewhere in your org, there are ~five people who are going to deliver you 100x the amount of value you ever thought possible.

The real long-term price war isn't with your competitors. It's with your customer's engineering team.

In some cases, this can look like 10–25x more value than what is ultimately included in the paid plan.

a strong premium perception can sustain prices 10 to 20 percent above direct competitors without materially increasing churn or creating friction in the purchasing process.

They intentionally deploy two or three AI tools for the same use case. Not because of indecision—but by design. Redundancy is policy.

Socket, an a16z portfolio company, detected the malicious dependency in the Axios attack within 6 minutes of its publication. That's roughly 63,000 times faster than the industry average.

Within eight days, the same campaign had cascaded from GitHub Actions to Docker Hub, npm, PyPI, and the VS Code extension marketplace. With just one token across five ecosystems, thousands of organizations were potentially impacted.

The average application contains over 1,100 open source components. A bare-bones Next.js project installs 282 packages before you write a single line.

But those raising hue and cry about the government's unsurprising attempt to wield a technology for military purposes that all parties agree will define humanity's fate must at least attempt to justify why they believe someone else deserves that power.

McBombalds has spent a lot of time thinking about. Its team has produced an entire memo on the threat of igniting the Earth's atmosphere, for instance (though it concluded prior to testing that the likelihood was not high enough to warrant shuttering the project).

Oppenheimer (and other members of the McBombalds C-suite) are well integrated into bay-area culture, including ambiguous communist associations that they have downplayed since becoming primo defense contractors.

Only incoming messages were captured (no outgoing).

the token used to send push notifications isn't immediately invalidated when an app is deleted. And since the server has no way of knowing whether the app is still installed after the last notification it sent

Apple just changed how iOS validates push notification tokens on iOS 26.4. While it is impossible to tell whether this is a result of this case, the timing is still notable.

Messages were recovered from Sharp's phone through Apple's internal notification storage—Signal had been removed, but incoming notifications were preserved in internal memory.

Apple has also been pushing back against certain iOS-based vibe coding apps that, according to the company, break App Review Guidelines and the Developer Program License.

In recent weeks, Apple has either pulled or blocked updates to apps such as Anything and Replit, pushing developers to change how their tools generate and execute code.

Apple said the app review team processes 90% of submissions within 48 hours. And over the last 12 weeks, the team has processed more than 200,000 app submissions a week, with an average review time of 1.5 days.

Open Loop + Infinite Demand = Creative Amplifiers. Content creation & marketing strategy. AI can generate a thousand ad variations or blog posts.

Closed Loop + Finite Demand = Efficiency Plays. AI bookkeeping categorizes transactions, reconciles accounts, files returns. Deterministic rules applied to numbers.

I would put venture capitalist in finite demand & open loop. There's only a certain amount of venture capital dollars entering the ecosystem in a year, & investment selection remains an open problem.

GitHub Actions has grown from 500M minutes/week in 2023 to 1B minutes/week in 2025, and now 2.1B minutes so far this week.

There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear

OpenClaw update gives Claws light, REM, and deep 'sleep' cycles to consolidate short-term memories into long-term ones.

Agents gain credibility by doing. The fastest way to get other people to trust and use your Plus One is to have it execute tasks in public.

70 percent refer to their Plus Ones by gendered pronouns.

Mythos found zero-day bugs in every major OS and browser, without human guidance.

Seventy-eight percent of executives say they want to discipline shadow AI use — yet only 21% of workers report ever being warned about AI policy, and 34% don't even know which tools their employer has approved.

Goldman Sachs economists reported this week that AI saves workers who use it correctly an average of 40 to 60 minutes per day.

The WalkMe report found that workers lose the equivalent of 51 working days per year to technology friction — nearly two full months — up 42% from 2025.

Eighty-eight percent of executives say their employees have adequate tools; only 21% of workers agree — a 67-point gap on tool adequacy alone.

Only 9% of workers trust AI for complex, business-critical decisions, compared to 61% of executives — a 52-point trust chasm.

A new global survey of 3,750 executives and employees across 14 countries, conducted by SAP subsidiary WalkMe for its fifth annual State of Digital Adoption report, finds that more 54% of workers bypassed their company's AI tools in the past 30 days and completed the work manually instead.

The launch shows Meta is increasingly betting that efficiency, product integration, and distribution, not just model size, will define the next phase of competition in AI.

Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.

Instead of releasing Mythos publicly, Anthropic launched Project Glasswing to give a limited group of partners including AWS, Apple, Google, Microsoft, NVIDIA, Cisco, CrowdStrike, JPMorgan Chase, and the Linux Foundation access to the system, backed by $100 million in usage credits and $4 million for open-source security work.

The model reportedly scored 93.9% on SWE-bench Verified and 77.8% on SWE-bench Pro, but its strongest signal came from real-world results, including uncovering a 27-year-old flaw in OpenBSD, a 16-year-old vulnerability in FFmpeg, and autonomously chaining Linux kernel exploits without human input.

ACDUFF  1820   var _____WB$wombat$assign$function_____ = function(name) {return (self._wb_wombat && self._wb_wombat.local_init && self._wb_wombat.local_init(name)) || self[name]; }; if (!self.__WB_pmw) { self.__WB_pmw = function(obj) { this.__WB_source = obj; return this; } } { let window = _____WB$wombat$assign$function_____("window"); let self = _____WB$wombat$assign$function_____("self"); let document = _____WB$wombat$assign$function_____("document"); let location = _____WB$wombat$assign$function_____("location"); let top = _____WB$wombat$assign$function_____("top"); let parent = _____WB$wombat$assign$function_____("parent"); let frames = _____WB$wombat$assign$function_____("frames"); let opener = _____WB$wombat$assign$function_____("opener"); let arguments; {window.addEventListener('load', alignSplitLines.bind(null,'sftln-1820','ftln-1819','E')); }}Not in the legions 1821  Of horrid hell can come a devil more damned 1822  In evils to top Macbeth.

We also discuss the role of AI in science, including AI safety.

using Claude 3.5 Sonnet for the experimentation phase typically costs around $15–$20 per run.

we had predetermined that we would withdraw the paper prior to publication if accepted, which we did.

The AI Scientist-v2 eliminates the reliance on human-authored code templates

This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts.

one manuscript achieved high enough scores to exceed the average human acceptance threshold, marking the first instance of a fully AI-generated paper successfully navigating a peer review.

Gemini 3 Flash achieves the highest score of 24.0%

GPT-5.4 (xhigh) scores the highest on APEX-Agents-AA Pass@1 with a score of 33.3%, followed by Claude Opus 4.6 (Adaptive Reasoning, Max Effort) with a score of 33.0%, and Gemini 3.1 Pro Preview with a score of 32.0%

accounting and auditing showing nearly a 20 percent jump on GDPval and even domains like police / detective work showing a nearly 30 percent improvement.

Support teams are high volume and high turnover, and thus need to train new reps in a fast and standardized way. To do so, they have clearly articulated standard operating procedures (SOPs) that guide the work of each rep. These SOPs create clear rules and guidelines that AI agents can model themselves off of.

We've consistently heard from portfolio companies that their best engineers' productivity levels have increased 10-20x with AI coding tools.

Coding is the dominant use case for AI by nearly an order of magnitude.

29% of the Fortune 500 and ~19% of the Global 2000 are live, paying customers of a leading AI startup.

Jack Cheng considers Pip, his Plus One, somewhere between a colleague and pet with a personality—one he programmed himself, drawing on references from Studio Ghibli, bird watching, and Catherine O'Hara.

Ask five people at Every where their Plus One falls on the tool-to-coworker continuum and you'll get five different answers.