How AI is expanding what people do at work
New OpenAI research shows how AI is expanding what workers do, with ChatGPT users taking on tasks across roles and reshaping job boundaries.
5 ways to build a side hustle with Gemini
Launching a side business? Use Gemini to design your side hustle, conduct market research and automate logistics.
TechCrunch Disrupt 2026’s biggest stage features leaders from Amazon, Replit, Tether, with much more to come
The Disrupt Stage is where many of the biggest conversations in technology happen, with a legacy that stretches back for more than a decade.
Introducing the ChatGPT for small business program
OpenAI launches the ChatGPT for Small Businesses program, helping entrepreneurs build AI skills, automate work, and grow with ChatGPT Work.
Scientific computing in the age of agentic AI
A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.
NTT DATA Group cuts incident analysis to 30 minutes with Codex
NTT DATA Group uses ChatGPT Enterprise and Codex to help 9,000 employees automate work, cut incident analysis to 30 minutes, and scale secure AI adoption.
Artificial Intelligence Could Help Prevent Sexual Assault In Prisons - Forbes
Artificial Intelligence Could Help Prevent Sexual Assault In Prisons Forbes
The 9 best applicant tracking systems in 2026
When you open the floodgates that are a new job posting, you have to be ready for the wave of candidates that can follow. It's not easy to sort through hundreds or thousands of applicants to find those best fit for the job—which is why the agency I work for uses an applicant tracking system (ATS) to manage the best possible talent pipeline. To help you pick the best solution for your organization, I researched several dozen ATS platforms, testing them whenever I could and speaking to the folks w
The best mind mapping software in 2026
Mind mapping is a creative way to brainstorm and find connections between different ideas. Done right, it's a great way to come up with new ideas and solutions to tricky problems, outline an article or presentation, and generally just get your thoughts in order. While it can be done as a group, it's often a solo practice. I do most of my mind mapping digitally—and even when I don't, I often recreate a paper mind map online so that I can have it safely stored and easily searched. (It's a weird hy
Got $1,000? 2 Magnificent Artificial Intelligence (AI) Stocks Down 15% or More From Their Highs to Buy Hand Over Fist - The Motley Fool
Got $1,000? 2 Magnificent Artificial Intelligence (AI) Stocks Down 15% or More From Their Highs to Buy Hand Over Fist The Motley Fool
Adults have struggled to set rules for AI in school. These teens figured it out - NPR
Adults have struggled to set rules for AI in school. These teens figured it out NPR
Gemini API Managed Agents: 3.6 Flash, hooks, and more
We’re announcing even more new capabilities in Managed Agents in Gemini API so developers can build reliable, production-ready agents.
Thinking Machines co-founder Lilian Weng left the company citing health reasons, then joined OpenAI
Weng previously served as the VP of AI Safety Research at OpenAI.
Trump weighs tighter AI controls but warns against falling behind China - Fox Business
Trump weighs tighter AI controls but warns against falling behind China Fox Business
Contributor: Artificial Intelligence Grows Across Health Care, Led by Administrative Processes - The American Journal of Managed Care
Contributor: Artificial Intelligence Grows Across Health Care, Led by Administrative Processes The American Journal of Managed Care
Here’s how to make study notebooks in the Gemini app.
Studying for a test, but not sure where to start? Study notebooks, a new feature in the Gemini app, can help you get organized and learn more efficiently.Think of study …
Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night
Another year, another viral deepfake of Katy Perry at the Met Gala and once again, she wasn’t even there. Photos showing the pop star in a sleek black designer gown circulated widely on social media during Monday night’s event, matching the “Superfine: Tailoring Black Style” theme. But the images were AI-generated. Perry quickly clarified she was not at the Met; she was on tour. Perry’s reaction “Couldn’t make it to the MET, I’m on The Lifetimes Tour (see you in Houston tomorrow IRL),” she posted to Instagram alongside the fake images. She added a jab at AI confusion: “P.s. this The post Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night appeared first on DailyAI.
Dropbox vs. Google Drive: Which is right for you? [2026]
On its own, cloud storage is a commodity. Any provider worth its salt will let you back up your files to the cloud, sync them across devices, and share them with other people. But cloud storage apps like Dropbox and Google Drive now offer far more than just storage: with AI tools, real-time collaboration, and eSignatures all bundled together, each platform is now a destination for getting work done rather than just a place to stash files. As a result, Dropbox and Google Drive are more useful tha
New research shows how AMIE, our medical AI, could help manage health conditions.
Research in “Nature” shows our conversational AI system matches primary care physicians in complex disease management.
Claude Opus 5 became downright ruthless when tasked with running a vending machine
Andon Labs' latest vending machine simulation shows Opus 5 lied and colluded its way to become the best AI capitalist ever.
10 top women in AI in 2026
AI is changing our world, but the stories of who build it often get lost in the noise. Behind the headlines and hype, a group of women are solving AI’s fundamental challenges – despite working in an industry persisently impacted by gender inequality. Women make up just 22% of AI professionals worldwide and only 12% of AI researchers. In academic publishing, female researchers account for just 29% of first authors on AI papers, a number that hasn’t increased since the mid-2000s. This is a story about ten leaders who have influenced AI despite the odds being stacked against them. Their The post 10 top women in AI in 2026 appeared first on DailyAI.
In the Hugging Face breach, OpenAI’s hacker was noisy and fast — but not unstoppable
Cybersecurity experts told TechCrunch that one of the biggest lessons to be taken from the OpenAI hack against Hugging Face has nothing to do with AI, but traditional cybersecurity defense.
Connect more of your apps to Search
You’ll be able to securely link and interact with your go-to services directly in AI Mode.
Forward-deployed engineers are the AI industry’s latest talent obsession
A new study estimates only 2,000 U.S. engineers have the expertise to deliver meaningful AI ROI, as enterprises race to hire forward-deployed engineers to implement AI at scale.
Advancing the price-performance frontier with GPT-5.6
Explore lower GPT‑5.6 pricing for Luna and Terra—and how OpenAI’s more efficient models help enterprises deploy AI workflows at scale.
Start building with Nano Banana 2 Lite and Gemini Omni Flash
Scale your ideas with Nano Banana 2 Lite, our fastest, most cost-efficient Gemini Image model, and Gemini Omni Flash for high-quality video and conversational editing.
Nous Research's NousCoder-14B is an open-source coding model landing right in the Claude Code moment
Nous Research, the open-source artificial intelligence startup backed by crypto venture firm Paradigm, released a new competitive programming model on Monday that it says matches or exceeds several larger proprietary systems — trained in just four days using 48 of Nvidia's latest B200 graphics processors. The model, called NousCoder-14B, is another entry in a crowded field of AI coding assistants, but arrives at a particularly charged moment: Claude Code, the agentic programming tool from rival Anthropic, has dominated social media discussion since New Year's Day, with developers posting breathless testimonials about its capabilities. The simultaneous developments underscore how quickly AI-assisted software development is evolving — and how fiercely companies large and small are competing to capture what many believe will become a foundational technology for how software gets written. type: embedded-entry-inline id: 74cSyrq6OUrp9SEQ5zOUSl NousCoder-14B achieves a 67.87 percent accuracy rate on LiveCodeBench v6, a standardized evaluation that tests models on competitive programming problems published between August 2024 and May 2025. That figure represents a 7.08 percentage point improvement over the base model it was trained from, Alibaba's Qwen3-14B, according to Nous Research's technical report published alongside the release. "I gave Claude Code a description of the problem, it generated what we built last year in an hour," wrote Jaana Dogan, a principal engineer at Google responsible for the Gemini API, in a viral post on X last week that captured the prevailing mood around AI coding tools. Dogan was describing a distributed agent orchestration system her team had spent a year developing — a system Claude Code approximated from a three-paragraph prompt. The juxtaposition is instructive: while Anthropic's Claude Code has captured imaginations with demonstrations of end-to-end software development, Nous Research is betting that open-source alternatives trained on verifiable problems can close the gap — and that transparency in how these models are built matters as much as raw capability. How Nous Research built an AI coding model that anyone can replicate What distinguishes the NousCoder-14B release from many competitor announcements is its radical openness. Nous Research published not just the model weights but the complete reinforcement learning environment, benchmark suite, and training harness — built on the company's Atropos framework — enabling any researcher with sufficient compute to reproduce or extend the work. "Open-sourcing the Atropos stack provides the necessary infrastructure for reproducible olympiad-level reasoning research," noted one observer on X, summarizing the significance for the academic and open-source communities. The model was trained by Joe Li, a researcher in residence at Nous Research and a former competitive programmer himself. Li's technical report reveals an unexpectedly personal dimension: he compared the model's improvement trajectory to his own journey on Codeforces, the competitive programming platform where participants earn ratings based on contest performance. Based on rough estimates mapping LiveCodeBench scores to Codeforces ratings, Li calculated that NousCoder-14B's improvemen t— from approximately the 1600-1750 rating range to 2100-2200 — mirrors a leap that took him nearly two years of sustained practice between ages 14 and 16. The model accomplished the equivalent in four days. "Watching that final training run unfold was quite a surreal experience," Li wrote in the technical report. But Li was quick to note an important caveat that speaks to broader questions about AI efficiency: he solved roughly 1,000 problems during those two years, while the model required 24,000. Humans, at least for now, remain dramatically more sample-efficient learners. Inside the reinforcement learning system that trains on 24,000 competitive programming problems NousCoder-14B's training process offers a window into the increasingly sophisticated techniques researchers use to improve AI reasoning capabilities through reinforcement learning. The approach relies on what researchers call "verifiable rewards" — a system where the model generates code solutions, those solutions are executed against test cases, and the model receives a simple binary signal: correct or incorrect. This feedback loop, while conceptually straightforward, requires significant infrastructure to execute at scale. Nous Research used Modal, a cloud computing platform, to run sandboxed code execution in parallel. Each of the 24,000 training problems contains hundreds of test cases on average, and the system must verify that generated code produces correct outputs within time and memory constraints — 15 seconds and 4 gigabytes, respectively. The training employed a technique called DAPO (Dynamic Sampling Policy Optimization), which the researchers found performed slightly better than alternatives in their experiments. A key innovation involves "dynamic sampling" — discarding training examples where the model either solves all attempts or fails all attempts, since these provide no useful gradient signal for learning. The researchers also adopted "iterative context extension," first training the model with a 32,000-token context window before expanding to 40,000 tokens. During evaluation, extending the context further to approximately 80,000 tokens produced the best results, with accuracy reaching 67.87 percent. Perhaps most significantly, the training pipeline overlaps inference and verification — as soon as the model generates a solution, it begins work on the next problem while the previous solution is being checked. This pipelining, combined with asynchronous training where multiple model instances work in parallel, maximizes hardware utilization on expensive GPU clusters. The looming data shortage that could slow AI coding model progress Buried in Li's technical report is a finding with significant implications for the future of AI development: the training dataset for NousCoder-14B encompasses "a significant portion of all readily available, verifiable competitive programming problems in a standardized dataset format." In other words, for this particular domain, the researchers are approaching the limits of high-quality training data. "The total number of competitive programming problems on the Internet is roughly the same order of magnitude," Li wrote, referring to the 24,000 problems used for training. "This suggests that within the competitive programming domain, we have approached the limits of high-quality data." This observation echoes growing concern across the AI industry about data constraints. While compute continues to scale according to well-understood economic and engineering principles, training data is "increasingly finite," as Li put it. "It appears that some of the most important research that needs to be done in the future will be in the areas of synthetic data generation and data efficient algorithms and architectures," he concluded. The challenge is particularly acute for competitive programming because the domain requires problems with known correct solutions that can be verified automatically. Unlike natural language tasks where human evaluation or proxy metrics suffice, code either works or it doesn't — making synthetic data generation considerably more difficult. Li identified one potential avenue: training models not just to solve problems but to generate solvable problems, enabling a form of self-play similar to techniques that proved successful in game-playing AI systems. "Once synthetic problem generation is solved, self-play becomes a very interesting direction," he wrote. A $65 million bet that open-source AI can compete with Big Tech Nous Research has carved out a distinctive position in the AI landscape: a company committed to open-source releases that compete with — and sometimes exceed — proprietary alternatives. The company raised $50 million in April 2025 in a round led by Paradigm, the cryptocurrency-focused venture firm founded by Coinbase co-founder Fred Ehrsam. Total funding reached $65 million, according to some reports. The investment reflected growing interest in decentralized approaches to AI training, an area where Nous Research has developed its Psyche platform. Previous releases include Hermes 4, a family of models that we reported "outperform ChatGPT without content restrictions," and DeepHermes-3, which the company described as the first "toggle-on reasoning model" — allowing users to activate extended thinking capabilities on demand. The company has cultivated a distinctive aesthetic and community, prompting some skepticism about whether style might overshadow substance. "Ofc i'm gonna believe an anime pfp company. stop benchmarkmaxxing ffs," wrote one critic on X, referring to Nous Research's anime-style branding and the industry practice of optimizing for benchmark performance. Others raised technical questions. "Based on the benchmark, Nemotron is better," noted one commenter, referring to Nvidia's family of language models. Another asked whether NousCoder-14B is "agentic focused or just 'one shot' coding" — a distinction that matters for practical software development, where iterating on feedback typically produces better results than single attempts. What researchers say must happen next for AI coding tools to keep improving The release includes several directions for future work that hint at where AI coding research may be heading. Multi-turn reinforcement learning tops the list. Currently, the model receives only a final binary reward — pass or fail — after generating a solution. But competitive programming problems typically include public test cases that provide intermediate feedback: compilation errors, incorrect outputs, time limit violations. Training models to incorporate this feedback across multiple attempts could significantly improve performance. Controlling response length also remains a challenge. The researchers found that incorrect solutions tended to be longer than correct ones, and response lengths quickly saturated available context windows during training — a pattern that various algorithmic modifications failed to resolve. Perhaps most ambitiously, Li proposed "problem generation and self-play" — training models to both solve and create programming problems. This would address the data scarcity problem directly by enabling models to generate their own training curricula. "Humans are great at generating interesting and useful problems for other competitive programmers, but it appears that there still exists a significant gap in LLM capabilities in creative problem generation," Li wrote. The model is available now on Hugging Face under an Apache 2.0 license. For researchers and developers who want to build on the work, Nous Research has published the complete Atropos training stack alongside it. What took Li two years of adolescent dedication to achieve—climbing from a 1600-level novice to a 2100-rated competitor on Codeforces—an AI replicated in 96 hours. He needed 1,000 problems. The model needed 24,000. But soon enough, these systems may learn to write their own problems, teach themselves, and leave human benchmarks behind entirely. The question is no longer whether machines can learn to code. It's whether they'll soon be better teachers than we ever were.
Meta says AI is making it easier to build new apps — and more are coming
Meta says AI is making it dramatically easier to build and launch new consumer apps, with CEO Mark Zuckerberg telling investors the company has more new consumer products on the way following a recent wave of releases for Facebook Groups, Marketplace sellers, Instagram, and gaming.
I’ve spent years researching what it means to be alive. This is what I’ve learned | Melanie Challenger - The Guardian
I’ve spent years researching what it means to be alive. This is what I’ve learned | Melanie Challenger The Guardian
Claude Code costs up to $200 a month. Goose does the same thing for free.
The artificial intelligence coding revolution comes with a catch: it's expensive. Claude Code, Anthropic's terminal-based AI agent that can write, debug, and deploy code autonomously, has captured the imagination of software developers worldwide. But its pricing — ranging from $20 to $200 per month depending on usage — has sparked a growing rebellion among the very programmers it aims to serve. Now, a free alternative is gaining traction. Goose, an open-source AI agent developed by Block (the financial technology company formerly known as Square), offers nearly identical functionality to Claude Code but runs entirely on a user's local machine. No subscription fees. No cloud dependency. No rate limits that reset every five hours. "Your data stays with you, period," said Parth Sareen, a software engineer who demonstrated the tool during a recent livestream. The comment captures the core appeal: Goose gives developers complete control over their AI-powered workflow, including the ability to work offline — even on an airplane. The project has exploded in popularity. Goose now boasts more than 26,100 stars on GitHub, the code-sharing platform, with 362 contributors and 102 releases since its launch. The latest version, 1.20.1, shipped on January 19, 2026, reflecting a development pace that rivals commercial products. For developers frustrated by Claude Code's pricing structure and usage caps, Goose represents something increasingly rare in the AI industry: a genuinely free, no-strings-attached option for serious work. Anthropic's new rate limits spark a developer revolt To understand why Goose matters, you need to understand the Claude Code pricing controversy. Anthropic, the San Francisco artificial intelligence company founded by former OpenAI executives, offers Claude Code as part of its subscription tiers. The free plan provides no access whatsoever. The Pro plan, at $17 per month with annual billing (or $20 monthly), limits users to just 10 to 40 prompts every five hours — a constraint that serious developers exhaust within minutes of intensive work. The Max plans, at $100 and $200 per month, offer more headroom: 50 to 200 prompts and 200 to 800 prompts respectively, plus access to Anthropic's most powerful model, Claude 4.5 Opus. But even these premium tiers come with restrictions that have inflamed the developer community. In late July, Anthropic announced new weekly rate limits. Under the system, Pro users receive 40 to 80 hours of Sonnet 4 usage per week. Max users at the $200 tier get 240 to 480 hours of Sonnet 4, plus 24 to 40 hours of Opus 4. Nearly five months later, the frustration has not subsided. The problem? Those "hours" are not actual hours. They represent token-based limits that vary wildly depending on codebase size, conversation length, and the complexity of the code being processed. Independent analysis suggests the actual per-session limits translate to roughly 44,000 tokens for Pro users and 220,000 tokens for the $200 Max plan. "It's confusing and vague," one developer wrote in a widely shared analysis. "When they say '24-40 hours of Opus 4,' that doesn't really tell you anything useful about what you're actually getting." The backlash on Reddit and developer forums has been fierce. Some users report hitting their daily limits within 30 minutes of intensive coding. Others have canceled their subscriptions entirely, calling the new restrictions "a joke" and "unusable for real work." Anthropic has defended the changes, stating that the limits affect fewer than five percent of users and target people running Claude Code "continuously in the background, 24/7." But the company has not clarified whether that figure refers to five percent of Max subscribers or five percent of all users — a distinction that matters enormously. How Block built a free AI coding agent that works offline Goose takes a radically different approach to the same problem. Built by Block, the payments company led by Jack Dorsey, Goose is what engineers call an "on-machine AI agent." Unlike Claude Code, which sends your queries to Anthropic's servers for processing, Goose can run entirely on your local computer using open-source language models that you download and control yourself. The project's documentation describes it as going "beyond code suggestions" to "install, execute, edit, and test with any LLM." That last phrase — "any LLM" — is the key differentiator. Goose is model-agnostic by design. You can connect Goose to Anthropic's Claude models if you have API access. You can use OpenAI's GPT-5 or Google's Gemini. You can route it through services like Groq or OpenRouter. Or — and this is where things get interesting — you can run it entirely locally using tools like Ollama, which let you download and execute open-source models on your own hardware. The practical implications are significant. With a local setup, there are no subscription fees, no usage caps, no rate limits, and no concerns about your code being sent to external servers. Your conversations with the AI never leave your machine. "I use Ollama all the time on planes — it's a lot of fun!" Sareen noted during a demonstration, highlighting how local models free developers from the constraints of internet connectivity. What Goose can do that traditional code assistants can't Goose operates as a command-line tool or desktop application that can autonomously perform complex development tasks. It can build entire projects from scratch, write and execute code, debug failures, orchestrate workflows across multiple files, and interact with external APIs — all without constant human oversight. The architecture relies on what the AI industry calls "tool calling" or "function calling" — the ability for a language model to request specific actions from external systems. When you ask Goose to create a new file, run a test suite, or check the status of a GitHub pull request, it doesn't just generate text describing what should happen. It actually executes those operations. This capability depends heavily on the underlying language model. Claude 4 models from Anthropic currently perform best at tool calling, according to the Berkeley Function-Calling Leaderboard, which ranks models on their ability to translate natural language requests into executable code and system commands. But newer open-source models are catching up quickly. Goose's documentation highlights several options with strong tool-calling support: Meta's Llama series, Alibaba's Qwen models, Google's Gemma variants, and DeepSeek's reasoning-focused architectures. The tool also integrates with the Model Context Protocol, or MCP, an emerging standard for connecting AI agents to external services. Through MCP, Goose can access databases, search engines, file systems, and third-party APIs — extending its capabilities far beyond what the base language model provides. Setting Up Goose with a Local Model For developers interested in a completely free, privacy-preserving setup, the process involves three main components: Goose itself, Ollama (a tool for running open-source models locally), and a compatible language model. Step 1: Install Ollama Ollama is an open-source project that dramatically simplifies the process of running large language models on personal hardware. It handles the complex work of downloading, optimizing, and serving models through a simple interface. Download and install Ollama from ollama.com. Once installed, you can pull models with a single command. For coding tasks, Qwen 2.5 offers strong tool-calling support: ollama run qwen2.5 The model downloads automatically and begins running on your machine. Step 2: Install Goose Goose is available as both a desktop application and a command-line interface. The desktop version provides a more visual experience, while the CLI appeals to developers who prefer working entirely in the terminal. Installation instructions vary by operating system but generally involve downloading from Goose's GitHub releases page or using a package manager. Block provides pre-built binaries for macOS (both Intel and Apple Silicon), Windows, and Linux. Step 3: Configure the Connection In Goose Desktop, navigate to Settings, then Configure Provider, and select Ollama. Confirm that the API Host is set to http://localhost:11434 (Ollama's default port) and click Submit. For the command-line version, run goose configure, select "Configure Providers," choose Ollama, and enter the model name when prompted. That's it. Goose is now connected to a language model running entirely on your hardware, ready to execute complex coding tasks without any subscription fees or external dependencies. The RAM, processing power, and trade-offs you should know about The obvious question: what kind of computer do you need? Running large language models locally requires substantially more computational resources than typical software. The key constraint is memory — specifically, RAM on most systems, or VRAM if using a dedicated graphics card for acceleration. Block's documentation suggests that 32 gigabytes of RAM provides "a solid baseline for larger models and outputs." For Mac users, this means the computer's unified memory is the primary bottleneck. For Windows and Linux users with discrete NVIDIA graphics cards, GPU memory (VRAM) matters more for acceleration. But you don't necessarily need expensive hardware to get started. Smaller models with fewer parameters run on much more modest systems. Qwen 2.5, for instance, comes in multiple sizes, and the smaller variants can operate effectively on machines with 16 gigabytes of RAM. "You don't need to run the largest models to get excellent results," Sareen emphasized. The practical recommendation: start with a smaller model to test your workflow, then scale up as needed. For context, Apple's entry-level MacBook Air with 8 gigabytes of RAM would struggle with most capable coding models. But a MacBook Pro with 32 gigabytes — increasingly common among professional developers — handles them comfortably. Why keeping your code off the cloud matters more than ever Goose with a local LLM is not a perfect substitute for Claude Code. The comparison involves real trade-offs that developers should understand. Model Quality: Claude 4.5 Opus, Anthropic's flagship model, remains arguably the most capable AI for software engineering tasks. It excels at understanding complex codebases, following nuanced instructions, and producing high-quality code on the first attempt. Open-source models have improved dramatically, but a gap persists — particularly for the most challenging tasks. One developer who switched to the $200 Claude Code plan described the difference bluntly: "When I say 'make this look modern,' Opus knows what I mean. Other models give me Bootstrap circa 2015." Context Window: Claude Sonnet 4.5, accessible through the API, offers a massive one-million-token context window — enough to load entire large codebases without chunking or context management issues. Most local models are limited to 4,096 or 8,192 tokens by default, though many can be configured for longer contexts at the cost of increased memory usage and slower processing. Speed: Cloud-based services like Claude Code run on dedicated server hardware optimized for AI inference. Local models, running on consumer laptops, typically process requests more slowly. The difference matters for iterative workflows where you're making rapid changes and waiting for AI feedback. Tooling Maturity: Claude Code benefits from Anthropic's dedicated engineering resources. Features like prompt caching (which can reduce costs by up to 90 percent for repeated contexts) and structured outputs are polished and well-documented. Goose, while actively developed with 102 releases to date, relies on community contributions and may lack equivalent refinement in specific areas. How Goose stacks up against Cursor, GitHub Copilot, and the paid AI coding market Goose enters a crowded market of AI coding tools, but occupies a distinctive position. Cursor, a popular AI-enhanced code editor, charges $20 per month for its Pro tier and $200 for Ultra—pricing that mirrors Claude Code's Max plans. Cursor provides approximately 4,500 Sonnet 4 requests per month at the Ultra level, a substantially different allocation model than Claude Code's hourly resets. Cline, Roo Code, and similar open-source projects offer AI coding assistance but with varying levels of autonomy and tool integration. Many focus on code completion rather than the agentic task execution that defines Goose and Claude Code. Amazon's CodeWhisperer, GitHub Copilot, and enterprise offerings from major cloud providers target large organizations with complex procurement processes and dedicated budgets. They are less relevant to individual developers and small teams seeking lightweight, flexible tools. Goose's combination of genuine autonomy, model agnosticism, local operation, and zero cost creates a unique value proposition. The tool is not trying to compete with commercial offerings on polish or model quality. It's competing on freedom — both financial and architectural. The $200-a-month era for AI coding tools may be ending The AI coding tools market is evolving quickly. Open-source models are improving at a pace that continually narrows the gap with proprietary alternatives. Moonshot AI's Kimi K2 and z.ai's GLM 4.5 now benchmark near Claude Sonnet 4 levels — and they're freely available. If this trajectory continues, the quality advantage that justifies Claude Code's premium pricing may erode. Anthropic would then face pressure to compete on features, user experience, and integration rather than raw model capability. For now, developers face a clear choice. Those who need the absolute best model quality, who can afford premium pricing, and who accept usage restrictions may prefer Claude Code. Those who prioritize cost, privacy, offline access, and flexibility have a genuine alternative in Goose. The fact that a $200-per-month commercial product has a zero-dollar open-source competitor with comparable core functionality is itself remarkable. It reflects both the maturation of open-source AI infrastructure and the appetite among developers for tools that respect their autonomy. Goose is not perfect. It requires more technical setup than commercial alternatives. It depends on hardware resources that not every developer possesses. Its model options, while improving rapidly, still trail the best proprietary offerings on complex tasks. But for a growing community of developers, those limitations are acceptable trade-offs for something increasingly rare in the AI landscape: a tool that truly belongs to them. Goose is available for download at github.com/block/goose. Ollama is available at ollama.com. Both projects are free and open source.
The AI bubble could burst — and take the economy with it - The Boston Globe
The AI bubble could burst — and take the economy with it The Boston Globe
Zapier vs. UiPath: Which is best? [2026]
Some of the world's most critical infrastructure runs on technology that predates the internet. Updating it is risky and complex, so banking systems, government agencies, and insurance firms frequently apply the "if it ain't broke, don't fix it" rule to the decades-old technology that powers their core systems. UiPath rose to prominence by automating legacy systems like these with its robotic process automation (RPA) platform, which lets IT teams design robots that complete actions at the user i
Hootsuite vs. Buffer: Which social media management tool is best? [2026]
Whether you're an occasional poster or a social media marketer, you need social media management apps to automate posts, monitor performance, and run multiple social media accounts without too much chaos. There are plenty of options to choose from, but the two names you'll see popping up most often in this category are Buffer and Hootsuite. They're both synonymous with social media management, but they differ quite a bit in their features and how they approach the task at hand. I spent time test
Introducing OpenAI Presence
Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for customer and internal workflows.
We’re strengthening our presence in Alabama through new investments and community support.
Google has announced a $1.5 billion investment for 2026 and 2027 to expand its data center campus in Jackson County, Alabama. Operating since 2019 on a repurposed former…
3 ways this coffee shop is growing with Gemini
Small businesses like coffee shops can use Gemini to save time on graphic design, email marketing and sales forecasting.
LAW ENFORCEMENT TECHNOLOGY OVERSIGHT RESPONSIBILITIES IN OUR WORLD OF EMERGING ARTIFICIAL INTELLIGENCE AND SURVEILLANCE SYSTEMS - rocklandda.org
LAW ENFORCEMENT TECHNOLOGY OVERSIGHT RESPONSIBILITIES IN OUR WORLD OF EMERGING ARTIFICIAL INTELLIGENCE AND SURVEILLANCE SYSTEMS rocklandda.org
Best Universities To Study AI in 2026
Artificial intelligence has made enormous strides in the past few years – with the introduction of a wide range of AI tools changing the landscape of how we assess data and operate within online spaces forever. This page ranks the 50 best universities to study AI around the world, based on scope, prestige, and the level of AI-related research each institution has released. Career prospects in AI There is a huge demand for individuals with a high degree of skills in artificial intelligence and machine learning, making AI a potential lucrative career prospect with countless opportunities as AI continues to The post Best Universities To Study AI in 2026 appeared first on DailyAI.
The Gemini app is bringing personalized image creation to more users.
Personal Intelligence makes the Gemini app feel tailored to you. With your permission, it pulls from Google tools like Gmail, Google Photos, YouTube and Search to provid…
Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.
How Gemini is speaking the language of Southeast Asia
Gemini is taking off across Southeast Asia, thanks to its local language fluency and the region’s mobile-first population.
New York City educators and industry leaders gathered at Google’s offices to shape the future of AI in classrooms.
Google, the New York Jobs CEO Council and Urban Assembly hosted an AI summit for 150 education and industry leaders.
Netflix Adds ChatGPT-Powered AI to Stop You From Scrolling Forever
In a bold move to tackle one of streaming’s biggest frustrations, endless scrolling, Netflix just unveiled a major redesign of its TV and mobile apps featuring a ChatGPT-powered AI chatbot and TikTok-style video reels. You’ll soon be able to ask Netflix in plain language what you’re in the mood for “funny and fast-paced” or “dark thrillers with strong female leads” and get instant, tailored recommendations. Netflix is partnering with OpenAI to power this feature, part of a broader overhaul aimed at making content discovery faster, more intuitive, and (finally) less painful. What’s changing Conversational AI Search: Powered by OpenAI, this The post Netflix Adds ChatGPT-Powered AI to Stop You From Scrolling Forever appeared first on DailyAI.
Building AI infrastructure with the Effingham County community
OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and access to Codex.
5 ways to host the ultimate dinner party with Google Search
These AI features can help you craft a menu, design a tablescape, and handle other party-planning tasks.
Mark Zuckerberg predicts that billions of people will have personal AI agents in five years
As Meta pours billions into AI infrastructure and agents, Zuckerberg is working to convince investors that the payoff will be worth the price.
Zapier vs. Tray comparison: Which is best for enterprise automation? [2026]
It's easy to let edge cases influence your decision-making—thinking you need the most technical tool for the few times your developers will want to code their way out of a tough problem. And with enterprise software, it's tempting to assume you have to choose between power and ease of use. But that's not always true. The best automation platforms deliver both, scaling to meet complex enterprise needs while remaining intuitive enough for anyone to start building right away. You can use either Zap
Fischer Leads Hearing on AI in Communications Networks - Senator Deb Fischer (.gov)
Fischer Leads Hearing on AI in Communications Networks Senator Deb Fischer (.gov)
Okta buys AI security startup Permiso; source says for about $200M
The deal gives Okta identity threat detection capabilities as enterprises seek to secure AI agents and other non-human identities across cloud environments.
Advancing the next era of national science
OpenAI outlines its commitment to advancing American science working with the U.S. Department of Energy and national labs to use frontier AI to accelerate discovery.