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Is Claude Opus 4.7 the Best AI Coding Model Right Now?

Liam Hayes
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Is Claude Opus 4.7 the Best AI Coding Model Right Now?

Quick Answer: Claude Opus 4.7 is Anthropic’s latest publicly available AI model, released April 15, 2026. It outperforms GPT-5.4 and Gemini 3.1 Pro on coding benchmarks and introduces stronger vision, document reasoning and agentic task execution

Anthropic released Claude Opus 4.7 on April 15, 2026 and it’s one of the more meaningful model updates we’ve seen this year. Having followed the Claude 4.x generation closely since its launch. This release stands out because the improvements are practical, not just benchmark numbers on a slide deck.

Here’s everything you need to know.

What Is Claude Opus 4.7?

Claude Opus 4.7 is Anthropic’s flagship AI model the most capable version available to the public right now. It sits at the top of the Claude lineup, above Sonnet and Haiku and is designed for complex coding, document analysis and autonomous multi-step tasks.

It’s available via Anthropic’s API and on Amazon Bedrock as of April 15, 2026.

What’s New in Claude Opus 4.7?

Anthropic focused this release on four areas that matter to developers and power users:

  • Agentic coding — Opus 4.7 plans multi-step software tasks, catches its own mistakes mid-run and completes complex workflows without needing constant input from the user
  • Vision and image reasoning — the model reads and interprets images, diagrams, charts and technical documents more accurately than Opus 4.6
  • Multi-tool use — it handles multiple APIs and tools simultaneously. Which is critical for anyone building AI-powered agents or automations
  • Document reasoning — Anthropic reports 21% fewer errors on dense document tasks compared to the previous version

These aren’t just spec sheet improvements. They translate to fewer corrections, cleaner outputs and faster results in real workflows.

How Does Claude Opus 4.7 Compare to GPT-5.4 and Gemini 3.1 Pro?

This is the question most people are asking, so let’s get straight to it.

BenchmarkClaude Opus 4.7GPT-5.4Gemini 3.1 Pro
SWE-bench Pro64.3%57.7%54.2%
SWE-bench Verified87.6%—80.6%
Finance Agent v1.164.4%61.5%59.7%

On every major coding and reasoning benchmark published so far, Claude Opus 4.7 leads the pack. The gap on SWE-bench Pro is nearly 7 points over GPT-5.4. That’s not marginal, that’s a clear performance advantage for software development use cases.

OpenAI has not published a comparable SWE-bench Verified score for GPT-5.4 at the time of writing.

What Is Project Glasswing And Why Does It Matter?

Claude Opus 4.7
image source- Claude ai

On April 7, 2026, Anthropic announced Project Glasswing, a major cross-industry cybersecurity initiative that puts AI at the center of software security.

The project is powered by Claude Mythos Preview — Anthropic’s unreleased frontier model and brings together roughly 50 organizations including AWS, Apple, Microsoft, Google, Nvidia and Cisco. The mission is straightforward: find and fix zero-day vulnerabilities in critical software before malicious actors do.

What makes this remarkable is scale. Mythos has already identified thousands of critical security flaws in major operating systems and browsers bugs that went undetected by traditional security tools for years.

Claude Opus 4.7 is a sibling to Mythos, built with the most sensitive cybersecurity capabilities deliberately restricted for safe public release. Mythos remains behind a verified access program.

Claude Code Desktop App — Now More Like a Real IDE

Alongside the model launch, Anthropic released a redesigned Claude Code desktop application. It now operates closer to a full integrated development environment, rather than a basic coding assistant. For developers using Claude as part of their daily build workflow. This is a meaningful upgrade in usability.

Anthropic is also reportedly developing an AI-powered design tool that generates websites, landing pages and presentations from plain-text prompts putting it in direct competition with Figma and Adobe.

Pricing — Same as Before

PlanInputOutput
Claude Opus 4.7$5 / million tokens$25 / million tokens

Pricing is unchanged from Opus 4.6. If you’re already on the Claude API, this upgrade costs nothing extra.

Should You Switch to Claude Opus 4.7?

If you’re already on Claude — yes, upgrade. The coding, document, and vision improvements are real and noticeable.

If you’re on GPT-5.4 or Gemini 3.1 Pro — if coding or agentic workflows are your priority, the benchmark gap is large enough to justify a serious test run.

If you’re evaluating AI tools for enterprise use — the Finance Agent benchmark lead and improved document reasoning make Opus 4.7 one of the stronger options available right now.

Anthropic is moving fast, and Claude Opus 4.7 is the clearest sign yet that they’re building the most competitive coding-focused model lineup in the industry.

FAQ

Q: What is Claude Opus 4.7?
A: Claude Opus 4.7 is Anthropic’s latest flagship AI model, released April 15, 2026, with improvements in coding, vision, document reasoning, and autonomous task execution.

Q: How does Claude Opus 4.7 compare to GPT-5.4?
A: On SWE-bench Pro, Claude Opus 4.7 scores 64.3% versus GPT-5.4’s 57.7%, giving Claude a clear lead in real-world coding benchmarks.

Q: What is Project Glasswing?
A: Project Glasswing is Anthropic’s cross-industry cybersecurity initiative, announced April 7, 2026, using Claude Mythos Preview to identify zero-day software vulnerabilities alongside partners like AWS, Apple, and Microsoft.

Q: How much does Claude Opus 4.7 cost?
A: Claude Opus 4.7 is priced at $5 per million input tokens and $25 per million output tokens — unchanged from Opus 4.6.

Learn about Project Glasswing in following article

Glasswing: The AI That Caught a 27-Year-Old Security Flaw Humans Completely Missed

What Is Mozilla Thunderbolt and Why Does It Matter for Enterprise AI in 2026?

Sophia Lin
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What Is Mozilla Thunderbolt and Why Does It Matter for Enterprise AI in 2026?

Mozilla has officially entered the enterprise AI race and it’s doing things differently from everyone else. MZLA Technologies Corporation, the for-profit subsidiary behind the widely trusted Thunderbird email client has launched Thunderbolt: an open-source AI client built specifically for organizations that want full control over their AI infrastructure.

If your organization has been holding off on AI adoption because of data privacy concerns, vendor lock-in or regulatory requirements. This is the launch you’ve been waiting for.

What Is Mozilla Thunderbolt?

Mozilla Thunderbolt is a self-hostable, open-source AI client that lets enterprises run AI workloads entirely on their own infrastructure. It connects employees to large language models (LLMs) for chat, search, research and task automation without routing sensitive data through third-party cloud servers.

Unlike tools such as Microsoft Copilot or ChatGPT Enterprise, Thunderbolt gives your IT team full control over which models are used. Where data is stored, and how AI is deployed across your organization.

Who Built Thunderbolt and Why Should You Trust It?

It is developed by MZLA Technologies Corporation, the for-profit subsidiary of the Mozilla Foundation. The same nonprofit organization behind the Firefox browser and Thunderbird email client, both trusted by hundreds of millions of users worldwide for over two decades.

Mozilla has a long-standing reputation for prioritizing user privacy and open-source transparency. Thunderbolt was funded through a direct investment from Mozilla and is being built by a dedicated team separate from the Thunderbird development group meaning it has focused resources behind it, not just a side project.

This isn’t a startup making bold promises. It’s a proven organization with a clear track record on privacy, open standards and community accountability.

What Can Thunderbolt Actually Do?

Mozilla Thunderbolt
image source- freepik.com

It serves as an enterprise-grade AI front end with the following core capabilities:

  • AI chat and task automation for everyday employee workflows
  • Research and search assistance powered by your chosen LLM
  • Backend inference proxy that routes LLM calls securely within your infrastructure
  • Multi-provider support including Anthropic, OpenAI, Mistral and OpenRouter
  • Ollama compatibility on the roadmap, enabling fully local, air-gapped model deployments
  • Cross-platform availability across web, Windows, macOS, Linux, iOS and Android

The emphasis throughout is on data ownership and avoiding vendor lock-in — two things that matter enormously in regulated industries like healthcare, finance and government.

How Does Thunderbolt Compare to Other Enterprise AI Tools?

FeatureThunderboltMicrosoft CopilotChatGPT Enterprise
Self-hostable✅ Yes❌ No❌ No
Open source✅ Yes❌ No❌ No
Vendor lock-in❌ None✅ High✅ High
Local model support✅ Planned❌ No❌ No
Data sovereignty✅ Full control⚠️ Partial⚠️ Partial

For organizations with strict data governance requirements. Thunderbolt’s architecture is a clear differentiator.

Is Thunderbolt Ready for Production Use?

Not yet — and Mozilla is being upfront about that. The project is currently under active development and undergoing a security audit before reaching enterprise production readiness. Early adopters can already access the codebase on GitHub and begin testing self-hosted deployments.

A hosted version for individual users is also planned, though no release date has been announced.

Our recommendation: If you’re an IT leader or decision-maker, now is a great time to assign a technical team member to evaluate the GitHub repository and begin scoping a pilot deployment. Getting familiar early puts you ahead of the curve.

Why Enterprise Demand for Self-Hosted AI Is Growing Fast

This launch is strategically well-timed. Analyst firm Gartner projects that 65% of governments will introduce technological sovereignty requirements by 2028. Regulated industries including healthcare, finance and legal are already facing pressure to demonstrate that AI tools don’t expose sensitive data to external vendors.

Mozilla’s parallel partnership with Mila, the Quebec Artificial Intelligence Institute, further signals a long-term commitment to sovereign and open-source AI — including research into portable memory architectures for AI agents.

Thunderbolt isn’t just a product launch. It’s Mozilla planting a flag in what may become the most important segment of the enterprise AI market.

Frequently Asked Questions About Mozilla Thunderbolt

What is Mozilla Thunderbolt used for?
It is used by enterprises to deploy AI chat, search and automation tools entirely within their own infrastructure, keeping sensitive data off third-party cloud servers.

Is Mozilla Thunderbolt free?
The open-source code is freely available and self-deployable. Mozilla plans to generate revenue through enterprise support and managed deployment contracts.

Which AI models does Thunderbolt support?
Currently Anthropic, OpenAI, Mistral, and OpenRouter are supported. Ollama support for local models is on the roadmap.

Is Thunderbolt production-ready?
Not yet. It is under active development and security audit as of April 2026.

How is Thunderbolt different from ChatGPT Enterprise?
It is self-hosted and open source, giving organizations full data ownership. ChatGPT Enterprise routes data through OpenAI’s cloud infrastructure.

Final Verdict: Should Your Organization Watch Thunderbolt?

Absolutely especially if data sovereignty, compliance or vendor independence is a priority for your organization. Mozilla isn’t trying to out-feature ChatGPT or Copilot. It’s solving a different problem: giving enterprises trustworthy, transparent and fully controlled AI infrastructure.

The open-source model means you can evaluate it on your own terms today. Keep Thunder-bolt on your radar this one has real potential to matter.

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How CREAO AI Lets You Run Your Business on Autopilot in 2026

How CREAO AI Lets You Run Your Business on Autopilot in 2026

Sophia Lin
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How CREAO AI Lets You Run Your Business on Autopilot in 2026

Quick Answer: CREAO AI is a no-code AI super agent platform that lets you build autonomous AI agents, automate business workflows and create full-stack web apps. All using plain English, zero coding required.

If you’ve been relying on ChatGPT for your daily tasks and still feel like you’re doing all the work. CREAO AI is the upgrade your workflow has been waiting for. It’s not just another chatbot. It’s a fully autonomous AI system that works for you around the clock, remembers your business and actually gets things done.

What Is CREAO AI?

CREAO AI is an AI-powered automation platform that enables individuals, freelancers, content creators and business teams to build custom AI agents and apps through simple, natural language conversation.

Unlike traditional AI tools that respond once and forget everything. CREAO is built around a Super Agent — an AI that remembers your business context, connects to your real-world tools and can be scheduled to execute tasks automatically without you having to prompt it every single day.

Its core mission is simple but powerful: move beyond the chat box and turn every successful AI-assisted workflow into a reusable, automated system that runs whether you’re at your desk or fast asleep.

How Does CREAO AI Work?

Getting started with CREAO doesn’t require any technical background. Here’s the step-by-step flow:

  1. Describe your task in plain English — just type what you need. Example: Pull my Google Analytics data and write a weekly performance summary.
  2. The Super Agent executes it end-to-end — handling research, writing, data analysis, code generation and file creation all within a single session.
  3. Save it as a reusable Agent App — that session becomes a repeatable workflow your whole team can run again without starting from scratch.
  4. Schedule it to run automatically — set it to execute daily, weekly, or on demand. Your AI works while you focus on everything else.

That fourth step is what makes CREAO fundamentally different from every other AI tool available right now.

Key Features of CREAO AI

CREAO AI
image source- creao ai

Persistent Business Memory
Unlike ChatGPT, which starts fresh every conversation. CREAO remembers your analytics benchmarks, client preferences, content naming conventions and past decisions and applies that context in every future session. The more you use it, the smarter it gets about your specific business.

Scheduled Agent Runs
This is CREAO’s standout feature. You can set workflows to execute on complete autopilot. A Monday morning competitor scan, a daily lead check. A weekly SEO performance report and they run without you ever opening the app. This is genuinely passive productivity.

Deep Tool Integrations
CREAO natively connects to the tools you’re already using: Google Sheets, Gmail, Google Analytics, Google Ads, Slack, Notion, GitHub, Outlook, Perplexity, Miro and more. Via APIs and Model Context Protocol (MCP). You can even expose your CREAO app as an MCP server to plug into tools like Claude or Cursor.

No-Code Full-Stack App Building
Have a business idea but no developer? CREAO builds complete web applications — frontend, backend, and database. Just from a plain English description. It handles multi-step workflows far beyond what typical no-code builders can manage.

Built-In AI Copilot for Every App
Every app that CREAO builds comes embedded with its own AI copilot — meaning the software itself is intelligent from day one, not just the builder tool.

Work-to-App Flywheel
Every workflow you build doesn’t disappear after one use. CREAO converts your best sessions into shareable Agent Apps your team can reuse, remix and scale. You’re not just automating tasks. You’re building a growing library of intelligent tools for your business.

Who Should Be Using CREAO AI?

CREAO AI is ideal for anyone managing recurring workflows who’s tired of doing the same tasks manually every week:

  • Content creators and bloggers who need automated content briefs, keyword research and performance reports
  • SEO consultants running weekly rank tracking, competitor analysis and client reporting
  • Digital entrepreneurs managing multiple websites, tools, or client accounts simultaneously
  • Freelancers who want to deliver agency-level output without hiring a full team
  • Small business owners who need custom internal tools but can’t afford developers
  • Startup founders who need fast prototypes without any engineering resources

If you’re running an online business and still doing your analytics, reporting, or content research manually. CREAO can take all of that off your plate.

CREAO AI vs. ChatGPT: What’s the Real Difference?

FeatureChatGPTCREAO AI
Primary UseGeneral-purpose chatbotBusiness workflow automation
MemoryBasic conversation recallPersistent, purpose-built business memory
IntegrationsLimitedGoogle Sheets, Gmail, GA4, Slack & more
SchedulingManual prompting every timeFully automated, runs 24/7
OutputText responsesReusable Agent Apps
App BuildingNoFull-stack web apps via natural language

The short answer: use ChatGPT when you need a smart conversation partner. Use CREAO AI when you need work to actually get done — automatically, repeatedly and connected to your real business tools.

Frequently Asked Questions About CREAO AI

Is CREAO AI free to use?
Yes. CREAO AI offers a free plan with 30 credits per month — no credit card required. Paid PRO plans are available for heavier automation, more integrations and larger team usage.

Does CREAO AI require coding skills?
No. CREAO AI is fully no-code. You describe what you want in plain language and the Super Agent builds, runs, and automates it for you.

What tools does CREAO AI integrate with?
CREAO integrates with Google Analytics, Google Ads, Google Sheets, Gmail, Slack, Notion, GitHub, Outlook, Perplexity, Miro and supports custom APIs and MCP servers.

Can CREAO AI build real web apps?
Yes. CREAO builds complete full-stack web applications including frontend, backend and database from a plain English description. Apps can also be exposed as MCP servers for use in other AI tools.

How is CREAO AI different from Zapier or Make?
Zapier and Make automate predefined, rule-based flows. CREAO AI uses an autonomous Super Agent with memory and intelligence. It can reason, adapt, create files, write code, and build apps, not just trigger pre-set actions.

CREAO AI Pricing

PlanPriceWhat You Get
Free$0/month30 credits/month, no credit card needed
PROPaidHigher usage, full integrations, team features

The free tier is a genuinely risk-free way to test your first automation and see real results before committing to a paid plan.

Final Verdict: Is CREAO AI Worth It in 2026?

CREAO AI represents a real shift in how online businesses can operate in 2026. It’s not about manually prompting AI every single day. It’s about building intelligent systems that run your workflows, remember your preferences and deliver tangible outputs like reports, spreadsheets and full web apps while you focus on growing your business.

For content creators, SEO specialists and digital entrepreneurs juggling multiple projects. The time savings alone make CREAO AI one of the most compelling tools you can add to your stack this year.

The free tier is the perfect place to start. Build one agent, automate one workflow and see how much time you win back.

👉 Try CREAO AI free at creao.ai

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OpenAI Launches GPT-5.4 Cyber And It’s Built Specifically for Defenders

OpenAI Launches GPT-5.4 Cyber And It’s Built Specifically for Defenders

Ethan Cole
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OpenAI Launches GPT-5.4 Cyber And It’s Built Specifically for Defenders

Cybersecurity professionals have always been fighting with one hand tied behind their back. Attackers only need to find one vulnerability. Defenders need to find them all. Now, OpenAI wants to change that equation.

On Tuesday, OpenAI unveiled GPT-5.4 Cyber. A specialized variant of GPT-5.4, built specifically for defensive cybersecurity work. It’s not a general-purpose assistant with a few security prompts bolted on. This is a purpose-built tool designed to give vetted security professionals access to capabilities that were previously too sensitive to release broadly.

What Makes GPT-5.4 Cyber Different?

Most AI models are deliberately restricted when it comes to security topics. Ask them to help analyze malware or reverse engineer a binary and they’ll often refuse or water down the response. GPT-5.4 Cyber flips that dynamic intentionally.

OpenAI describes GPT-5.4 Cyber as purposely fine-tuned for additional cyber capabilities and with fewer capability restrictions than its standard releases. One of its most powerful features is binary reverse engineering. The ability to analyze compiled software for vulnerabilities, malware signatures and security weaknesses without needing the original source code. For incident responders and threat analysts, that’s a game-changer.

GPT-5.4 Cyber is being rolled out through OpenAI’s expanded Trusted Access for Cyber program. Which requires identity verification and limits usage to vetted security professionals. Individual researchers can verify through a dedicated portal; enterprise teams can apply through their OpenAI account representative.

Why OpenAI Is Releasing GPT-5.4 Cyber Now

This launch didn’t happen in a vacuum. OpenAI was direct about the timing: In preparation for increasingly more capable models from OpenAI over the next few months, we are fine-tuning our models specifically to enable defensive cybersecurity use cases. As model capabilities increase, defenses need to scale alongside them.”

That’s a candid acknowledgment that more powerful AI on both sides is coming fast. Rather than waiting, OpenAI is trying to give defenders a head start with GPT-5.4 Cyber before the threat landscape gets worse.

The Trusted Access for Cyber program first launched in February, backed by $10 million in API credits for participants. GPT-5.4 Cyber represents its most significant evolution yet — moving from a resource program into a fully capability-unlocked model with real-world security applications.

GPT-5.4 Cyber vs Anthropic’s Claude Mythos Preview

GPT-5.4
image source- Anthropic

OpenAI isn’t alone in this space. Just one week earlier, Anthropic launched Claude Mythos Preview through its own restricted-access initiative. Project Glasswing — granting access to more than 40 hand-selected organizations, including Amazon, Apple, Microsoft, and CrowdStrike.

But the two companies have taken fundamentally different approaches to the same problem:

GPT-5.4 Cyber (OpenAI)Claude Mythos Preview (Anthropic)
Access ModelVerification-based, broadly availableHand-selected consortium
Program NameTrusted Access for CyberProject Glasswing
Core PhilosophyEnable as many defenders as possibleControlled, curated access
Funding Support$10M in API creditsNot disclosed

Anthropic’s model has been described by analysts as relying on manual decisions about access. A tightly controlled approach that prioritizes accountability over reach. OpenAI pushes back on that logic entirely: We don’t think it’s practical or appropriate to centrally decide who gets to defend themselves.

The Threat Is Already Real

This isn’t theoretical. During internal testing, Anthropic’s Mythos Preview autonomously identified thousands of zero-day vulnerabilities — including a 27-year-old flaw in OpenBSD and a 16-year-old bug in FFmpeg’s H.264 codec. Anthropic has reportedly briefed government officials on the risks, warning that AI capabilities at this level could make large-scale cyberattacks more likely in the near term.

That context matters. These models are already capable of finding the kinds of vulnerabilities that have sat undetected for decades. Getting them into defenders’ hands isn’t optional anymore. It’s urgent.

What GPT-5.4 Cyber Means for Security Teams

If you work in cybersecurity whether you’re a threat analyst. A penetration tester, or a SOC team lead — GPT-5.4 Cyber marks a shift worth paying attention to. AI-assisted defense is no longer a future capability. It’s available now, and the barrier to access is getting lower.

The era of AI-powered cyber defense has officially begun. The only question is whether your team is ready to use it.

Lovable AI Review 2026: Can You Really Build an App Without Coding?

Kaus
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Lovable AI Review 2026: Can You Really Build an App Without Coding?

A few months ago, a friend of mine a marketing manager with zero coding background showed me a SaaS tool she’d built over a weekend its Islamic prayer today. It had user login, a live database and a clean UI that honestly looked better than some paid tools I’ve used. She built it entirely using Lovable AI.

That got my attention.

Lovable has been one of the most talked-about tools in the tech world lately and the numbers back up the buzz. The platform recently raised $330 million at a $6.6 billion valuation has over 25 million projects created on it. And is adding around 100,000 new projects every single day. So the question isn’t whether people are using it. The question is whether it’s actually worth your time.

I spent several weeks testing it across different project types. Here’s the honest breakdown.

What Is Lovable AI, Exactly?

Lovable is an AI-powered full-stack app builder. You type what you want in plain English “build me a project management tool with a login page and a kanban board” and Lovable generates a working web application. Not a mockup. Not a wireframe. An actual, deployable app with a frontend, backend, database and authentication already wired together.

Under the hood, it uses React for the interface, Tailwind CSS for styling and Supabase for the backend. A tech stack that professional developers use in production every day. That matters because it means the code Lovable generates is real code you can own, export to GitHub and hand off to a developer if you ever need to extend it.

This is what separates Lovable from traditional no-code tools. You’re not dragging blocks around a canvas. You’re getting actual code that runs.

Who Is It Actually Built For?

Lovable is genuinely most useful for three types of people:

Non-technical founders who have an idea they want to validate quickly without hiring a developer or spending months learning to code. Lovable can take you from concept to working prototype in hours, not weeks.

Product managers and designers who want to build interactive demos or functional prototypes that they can actually put in front of users or investors not static Figma mockups.

Developers who want to move faster. Lovable handles all the boilerplate — authentication, database setup, routing, deployment. So you can skip straight to the logic that actually makes your product unique.

If you’re a non-technical person building your first app. This is genuinely one of the most approachable tools available in 2026.

What It Does Well

Lovable AI
image source- lovable ai

The speed is real. I described a basic CRM with contact management and email logging and Lovable had a working prototype in under 15 minutes. The UI looked polished. The database was connected and the login system worked out of the box.

The Plan Mode feature (added in early 2026) is particularly useful — before writing any code. Lovable shows you a structured plan of what it intends to build. You can review, adjust and approve before it touches a line of code. For anyone who’s had an AI tool go off in the wrong direction and waste your time, this is a genuine improvement.

GitHub sync means you’re never locked in. Your code exports cleanly to your own repository and you can host it anywhere Vercel, Netlify, your own server. Lovable’s integrations with Stripe, Supabase and authentication tools like Clerk mean the most common app requirements are handled natively.

Where It Falls Short

Here’s the honest part.

The credit system is where things get frustrating. Every interaction with the AI costs credits. Simple UI tweaks cost around half a credit. Adding authentication costs around 1.2 credits. Building a basic MVP typically burns through 150 to 300 credits over a few weeks of iteration. The problem is the interface doesn’t tell you upfront how many credits an action will use you find out after.

Debugging is the real credit killer. When the AI gets stuck on an error and keeps trying the same fix in loops. Your monthly credit allocation can disappear faster than expected. Some users on community forums report that tasks costing one credit previously now consume five after platform updates.

The free plan gives you just 5 credits per day — enough to explore the platform and generate a basic prototype, but not enough to build anything serious. You’ll want the Starter plan ($20/month for 100 credits) before you attempt a real project.

Lovable is also web-only. If you need native iOS or Android apps, you’ll need a different tool.

Pricing at a Glance

PlanPriceCreditsBest For
Free$05/dayTesting the platform
Starter~$20/month100/monthBuilding one focused MVP
Pro~$25/month100/month + extrasSolo developers, freelancers
Business~$50/month100/month + team featuresSmall teams, startups

One practical tip: start on the free plan and learn how to write clear, detailed prompts before upgrading. The more specific your instructions, the fewer iterations you need and the fewer credits you burn.

How Does It Compare?

Lovable vs Bolt.new: Bolt gives you more technical flexibility and is better if you want direct control over the code. Lovable is more beginner-friendly with a cleaner out-of-the-box experience. Designers tend to prefer Lovable; developers often prefer Bolt.

Lovable vs v0 (Vercel): v0 generates excellent production-grade Next.js output, but it assumes you’re already comfortable with React. Lovable is far more accessible for non-coders.

Lovable vs Replit: Replit gives you a full coding environment with more transparency into the code. If you want to understand what’s being built, Replit is better. If you want results fast without touching code, Lovable wins.

The Verdict

Lovable is the fastest way to go from an idea to a working web app in 2026 — and that is a genuinely powerful thing. The gap it closes between I have an idea and I have something I can show people used to take weeks and thousands of dollars. Now it takes an afternoon.

But go in with realistic expectations. It’s excellent for MVPs, prototypes and idea validation. It struggles with complex backend logic. And the credit system requires careful management if you’re on a limited budget.

Use Lovable if: You need to build something fast, you want to own the code, and your project is a web app with standard features.

Look elsewhere if: You need native mobile apps, complex multi-step workflows, or fully predictable monthly costs.

Start with the free plan, build something small and see how far a single weekend takes you. That experience will tell you more than any review can.

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Glasswing: The AI That Caught a 27-Year-Old Security Flaw Humans Completely Missed

Glasswing: The AI That Caught a 27-Year-Old Security Flaw Humans Completely Missed

Kaus
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0
Glasswing: The AI That Caught a 27-Year-Old Security Flaw Humans Completely Missed

Cybersecurity experts feared AI-powered hacking for years. Anthropic’s Project Glasswing just flipped that fear into a defense strategy and the results are already hard to ignore.

I’ll be honest with you. When I first heard AI-powered cybersecurity initiative. my instinct was to skim past it. We’ve seen plenty of announcements dressed up as breakthroughs. But then I read one specific detail about Glasswing and I couldn’t move on.

Claude Mythos Preview is the specialized model Anthropic built for this project. It found a vulnerability that had been hiding inside OpenBSD for 27 years. OpenBSD is not a niche tool. It powers critical servers globally. Security professionals, penetration testers and some of the sharpest engineering teams in the world had reviewed that code repeatedly. Nobody caught it.

That’s not a headline. That’s a wake-up call.

What Project Glasswing Actually Is

Launched in April 2026, Project Glasswing is Anthropic’s initiative to use AI to proactively find and fix vulnerabilities in the world’s most widely used software and open-source infrastructure.

It is not a product you can buy. It is closer to a mission with a coalition behind it. Apple, Google, Microsoft, Amazon and NVIDIA are all involved. These are direct competitors who rarely share the same stage for anything other than earnings calls. The fact that they are cooperating on this tells you something important about how serious the underlying problem is.

Anthropic is also backing it financially. They committed $100 million in model usage credits and $4 million in direct funding to open-source security organizations. That is not a marketing budget. That is a signal of long-term commitment.

The Bug That Should Make Everyone Pay Attention

glasswing
image source- claude

Let me put the 27-year-old OpenBSD vulnerability into context.

OpenBSD has a reputation as one of the most security-focused operating systems ever built. Its developers are meticulous. Its codebase gets reviewed obsessively. Yet a flaw introduced nearly three decades ago sat there undetected through countless audits.

Claude Mythos also flagged a 16-year-old vulnerability in FFmpeg. FFmpeg is a library embedded in nearly every platform that handles video, from social media apps to video editing software to streaming services.

Two flaws. Decades old. Missed by humans. Found by an AI model in a comparatively short window of time.

This does not mean human security researchers have been doing poor work. It means the sheer volume and complexity of modern software has outgrown what human teams can manually audit at scale. AI does not replace security expertise. It extends the reach of that expertise into places humans simply do not have the bandwidth to look.

The Tension Worth Talking About

Here is what a lot of coverage is dancing around. If Claude Mythos Preview is this good at finding vulnerabilities. It could theoretically be used to exploit them too.

Anthropic’s response is restricted access. Only around 50 vetted organizations are currently working with the model. That is a reasonable starting position. But anyone who has watched how AI capabilities have evolved over the last few years knows that restricted access is often a temporary state.

The dual-use problem is the defining tension of AI in security. The same capability that defends can also attack. Glasswing is the most credible attempt yet to build a defense-first framework before that tension becomes a crisis. Whether the governance holds up over time is a question worth watching closely.

Why This Is Personal, Not Just Corporate

You might be wondering what a coalition of tech giants and an Anthropic AI model has to do with your actual life.

The answer is everything running on your device right now.

The software libraries Glasswing is scanning are not abstract backend systems. They are embedded in your browser, your operating system, your apps and the services your bank uses. The flaws Claude found had been present and undetected for years in infrastructure that hundreds of millions of people depend on daily.

Glasswing is not solving a corporate problem. It is patching the foundation of the internet most of us take for granted.

What This Tells Us About AI’s Real Role in Security

The narrative around AI and cybersecurity has always leaned toward risk. AI will automate attacks. AI will write malware. AI will make hackers more dangerous.

That risk is real. But Glasswing makes the counter-argument with evidence, not theory. When deployed with clear intent and proper oversight. AI can do something human teams genuinely cannot. It can audit decades of complex code at scale without fatigue and without gaps.

The question going forward is not whether AI belongs in cybersecurity. It is already there. The question is who controls it, how it is governed and whether initiatives like Glasswing set the right precedent before the less careful versions arrive.

If a 27-year-old bug just got found in 2026, you have to ask: what else are we still sitting on?

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Meta Launches Muse Spark: The $14.3 Billion Bet on Catching OpenAI

Sophia Lin
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Meta Launches Muse Spark: The $14.3 Billion Bet on Catching OpenAI

If you’ve been watching the AI race heat up over the past year, you already know Meta has been playing catch-up. OpenAI has GPT-5. Google has Gemini. Anthropic has Claude. And Meta? Well, as of April 7, 2026, Meta finally has its answer and it’s called Muse Spark.

This isn’t just another model update. It’s the first AI model to come out of Meta Superintelligence Labs. The company’s newly formed elite research division. And the way it was built and who built it tells you a lot about how serious Meta is this time around.

So, What Exactly Is Muse Spark?

Think of Muse Spark as Meta’s attempt to rebuild its entire AI foundation from scratch. The model is designed to be fast without sacrificing depth. It handles both text and images, meaning you can drop a photo into a conversation and get genuinely useful, detailed responses not just a generic caption.

What makes it different from past Meta AI efforts is the flexibility it offers users. You get two modes right out of the gate:

  • Instant mode — fast, conversational answers for everyday questions
  • Thinking mode — slower, more deliberate reasoning for complex topics like math, science and health

A third mode called Contemplating is reportedly on the way for even deeper problem-solving tasks. That kind of layered approach is smart. It mirrors how humans naturally shift between quick intuition and careful analysis depending on the situation.

The Team That Built It

Here’s where the story gets interesting. Muse Spark was built by Meta Superintelligence Labs. A division Mark Zuckerberg stood up in mid-2025 after growing frustrated that Meta’s AI progress wasn’t keeping pace with competitors.

To lead it, Meta brought in Alexandr Wang the founder and former CEO of Scale AI through a deal that included a $14.3 billion investment in Scale AI for a 49% stake. Co-leading the lab is Nat Friedman, former CEO of GitHub. Both bring serious technical credibility to the table.

The talent recruitment didn’t stop there. Reports indicate some engineers on the team were offered pay packages worth hundreds of millions of dollars. Meta was clearly willing to spend whatever it took to close the gap fast.

What Can It Actually Do?

Beyond the two reasoning modes, Muse Spark comes packed with features that make it genuinely useful in day-to-day life:

  • Real-time image analysis — point it at a food label, a medical chart or a product photo and get an intelligent breakdown
  • Health-focused reasoning — developed with input from medical professionals. It can interpret health-related visuals and answer nuanced questions with care
  • Multi-agent orchestration — the model can spin up multiple AI sub-agents working in parallel to tackle complicated, multi-step problems faster
  • Thought compression — a reinforcement learning technique that trains the model to reason deeply first. Then deliver answers efficiently without unnecessary verbosity

That last point matters for real-world usage. Nobody wants an AI that takes 30 seconds to respond with a wall of text. Muse Spark is designed to think hard and speak concisely.

Is It Actually Competitive?

Muse Spark
image source- official meta

Fair question and the honest answer is: it’s close, but not quite at the top yet.

Independent benchmarks from Artificial Analysis place Muse Spark at 52 on the Intelligence Index, putting it just behind Gemini 3.1 Pro, GPT-5.4 and Claude Opus 4.6. That’s a solid debut, especially considering that just a few months ago Meta was reportedly considering licensing Google’s Gemini models because its own internal models weren’t cutting it.

The fact that Meta achieved comparable capability to Llama 4 Maverick while using over ten times less compute is also worth noting. That’s not just impressive. It’s the kind of efficiency that makes scaling up to more powerful future models much more viable.

What Comes Next?

Muse Spark is just the beginning of what Meta is calling the Muse series. A new line of proprietary models distinct from its open-source Llama family. Meta has said future versions will eventually be open-sourced, though the current release stays closed.

The model is live now at meta.ai and inside the Meta AI app with rollout to WhatsApp, Instagram, Facebook and Meta’s Ray-Ban smart glasses coming in weeks.

One thing to keep an eye on: to use Muse Spark, you’ll need a Facebook or Instagram account. For anyone concerned about data privacy, that login requirement is worth thinking about before diving in.

Meta isn’t claiming Muse Spark beats everyone else. But after a rough year of delays and missed benchmarks, launching a model that genuinely competes at the frontier level built by a dream team assembled at enormous cost is a statement. The Muse era has started. Whether it leads somewhere transformative depends entirely on what Meta builds next.


Are AI Overviews Actually Accurate? The Data Might Surprise You

Kaali Gohil
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Are AI Overviews Actually Accurate? The Data Might Surprise You

AI Overviews are now one of the first things you see when you search on Google. Before you even scroll, an AI-generated summary tells you the answer. Fast. Convenient. But accurate? That’s where things get complicated and the data is more alarming than most users realize.

What Are AI Overviews?

Google AI Overviews are AI-generated summaries powered by Google’s Gemini model that appear at the very top of search results. Rather than showing links, Google reads multiple web pages and writes a short answer on your behalf.

According to WordStream’s 2025 data, AI Overviews now show up on almost 55% of all Google searches and since the March 2025 core update. Their presence has grown by 115%. That makes them impossible to ignore, which is exactly why their accuracy matters so much.

The Numbers Behind the Accuracy Problem

The stats on AI Overview reliability are hard to brush off.

A BBC study that tested four major AI assistants — ChatGPT, Copilot, Gemini, and Perplexity across 100 real-world news queries found that over 51% of all responses had significant issues. About 19% contained outright factual errors such as wrong dates and incorrect figures and 13% of quoted material either didn’t match the original source or was completely fabricated.

A separate study on AI-generated scientific summaries found that even when summaries scored 92.5% accurate on paper, key nuances were frequently stripped away leaving readers with an incomplete or misleading picture. Even more troubling, research showed that between 26% and 73% of AI summaries introduced errors by exaggerating conclusions.

Why Does This Happen?

AI Overviews don’t actually know things — they predict what sounds right based on patterns. A massive audit of over 400,000 AI Overviews found that 77% of them cited sources only from the top 10 organic results, creating an echo chamber. If those top-ranked pages are outdated or wrong, the AI summary inherits those flaws.

Several factors drive inaccuracy:

  • Outdated sources — AI pulls from what’s most visible online, not what’s most recent or correct
  • Overgeneralization — complex, nuanced findings get condensed into bold, oversimplified statements
  • Hallucinations — the AI invents details to fill gaps, with the same confident tone as accurate information
  • Bias toward consensus — popular answers get amplified even when they’re factually wrong

The User Trust Paradox

Here’s where it gets interesting: users trust AI Overviews even when they probably shouldn’t.

WordStream data shows that 70% of consumers say they somewhat trust generative AI search results. At the same time, 75% of those same consumers are concerned about misinformation from AI. People know the risk exists, yet they still take AI summaries at face value.

Making it worse AI Overviews now take up 42% of the desktop screen and 48% of mobile screens. Users who don’t scroll past them are only reading about 30% of the AI Overview’s actual content. That’s a recipe for misunderstanding.

A Pew Research study found that users encountering AI Overviews are 50% less likely to click on the accompanying links — meaning fewer people are ever reaching the original, verified source.

What Google Actually Says

AI Overviews
image source- freepik.com

Google’s official position is that AI Overviews perform “on par” with traditional Featured Snippets. The company also says it has continually improved quality through core updates. In May 2025, Google even expanded AI Overviews to 200 countries and 40 languages.

But here’s the irony: Google still includes a disclaimer on every AI Overview — results may not be accurate. Even they’re not fully standing behind it. And given that 58% of Google searches now result in zero clicks, millions of people are walking away with AI-generated answers they never verified.

When to Trust Them (and When Not To)

AI Overviews aren’t useless — they just need to be used correctly:

  • ✅ Lower risk: Basic definitions, general how-to questions, well-established facts
  • ⚠️ Medium risk: News summaries, recent events, industry-specific topics
  • ❌ High risk: Medical, legal, financial decisions, or anything where being wrong has consequences

What This Means for Content Creators

For publishers and SEO professionals, AI Overviews are both a threat and an opportunity. Sites that rank in the top 50 domains on Google capture nearly 30% of all AIO mentions, meaning authority matters more than ever. Structured, well-cited, human-expert content is exactly what Google pulls from to build its summaries.

Write content that directly answers questions, builds genuine EEAT signals and cites credible sources and you won’t just survive the AI Overview era. You’ll be part of it.

Bottom line: With over half of AI-generated summaries showing accuracy issues in independent studies, treating AI Overviews as a starting point — not a final answer — is the smartest habit you can build right now.

Sources

Arc Browser Is Dead in 2026 — What Every User Needs to Know Right Now

Arjun Patel
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Arc Browser Is Dead in 2026 — What Every User Needs to Know Right Now

Quick Answer: Yes, Arc browser has been officially discontinued. The Browser Company stopped all active development in May 2025 and is now entirely focused on building Dia — an AI-first browser built for 2026 and beyond. If you’re still using Arc, here’s everything you need to know before making your next move.

If you searched Arc browser 2026 and landed here, you’re not alone. Thousands of Arc users are asking the same questions right now and the answers have changed significantly over the past year. We’re covering this now because Dia. Arc’s AI-powered successor, just received a major feature upgrade in early 2026 pulling in Arc’s best-loved tools. The browser landscape is actively shifting and staying ahead of it matters for anyone who lives in their browser all day.

Let’s answer every question clearly.

Is Arc Browser Being Discontinued?

Yes — Arc browser is officially discontinued. In May 2025, The Browser Company CEO Josh Miller published an open letter confirming that active development on Arc had permanently stopped. The entire team pivoted to Dia, a new AI-native browser designed to be simpler and smarter than Arc ever was.

The reason was data-driven and honest. Despite Arc having a devoted fanbase, key features never gained traction. Spaces one of Arc’s most praised tools were only used by 5–12% of users. The calendar preview? Just 0.4%. When you’re losing $30 million per year building features almost no one uses, something has to change.

The Browser Company made the call. Arc is done. Dia is the future.

Is Arc a Dead Browser in 2026?

Technically no — but practically, yes. Arc is now in maintenance mode, meaning it still receives Chromium-based security patches to keep your browsing safe. But zero new features are coming. No roadmap. No updates. Just security fixes until that eventually stops too.

The situation got more complex in September 2025 when Atlassian. The company behind Jira and Confluence — acquired The Browser Company for a reported $610 million. Every engineer, designer and product manager is now focused on building Dia under Atlassian’s umbrella. Arc is an orphan product keeping the lights on, nothing more.

The company has also discussed selling or open-sourcing Arc, but nothing has been confirmed. The core challenge? Arc is built on the same internal SDK powering Dia handing it over means giving away competitive technology.

If you’re starting fresh in 2026, Arc is not the browser to build your workflow around.

Is Arc Still a Good Browser to Use?

Yes — if you’re already on it. No — if you’re just starting out.

For current Arc users, there’s no emergency. The browser still works beautifully. The sidebar tab management, built-in ad blocking, split view and privacy-first design still beat most mainstream browsers in day-to-day comfort. Since it supports all Chrome extensions, there’s no functionality gap either.

But still good today and worth committing to are two different things. Every month that passes, Arc falls further behind competitors that are actively adding AI features, improving performance and building for how people browse in 2026.

Who should stay on Arc: Power users who’ve already built their workflow around it and aren’t ready to switch.

Who should move on: Anyone setting up a new device, building a new workflow or wanting a browser with a future.

The best alternatives right now are Dia (built by the same team, carries Arc’s DNA), Zen Browser (open-source, Firefox-based, closest to Arc’s interface with 40,000+ GitHub stars), and Brave (leaner, privacy-focused, actively developed).

Is Arc Slower Than Chrome?

In benchmarks, slightly — in real life, barely noticeable.

Both Arc and Chrome are built on the same Chromium engine, so their raw speed is nearly identical. In Speedometer 2.0 tests, Chrome scores around 564 runs per minute versus Arc’s 513 a difference most users will never feel while browsing.

Where the gap becomes real is RAM. Arc’s main process uses approximately 405MB of memory compared to Chrome’s 255MB. That’s a meaningful difference if you’re running a lot of tabs, working on an older machine or trying to preserve battery life on a laptop.

For most people on modern hardware, Arc and Chrome feel the same. If your machine is already under strain, Chrome or Brave will give you a smoother experience without sacrificing much.

What’s Coming Next: Meet Dia

Arc Browser
image source- dia

Dia launched publicly for Mac users in October 2025 — no invite needed. Built on Chromium. It brings a clean familiar interface with one major difference: the URL bar doubles as an AI chatbot. You can search the web, summarize open tabs, upload files for analysis and get AI-powered answers without switching to a separate tool like ChatGPT or Perplexity.

In early 2026, founder Josh Miller confirmed Dia is inheriting Arc’s greatest hits — the sidebar mode, vertical tabs and custom shortcuts Arc users loved. While stripping away everything that made Arc too complicated for mainstream use. Apple’s former Safari lead designer also joined the team in January 2026, signaling a serious commitment to design quality going forward.

Dia Pro is available for power users who want advanced AI features. The free tier covers everyday browsing comfortably.

Windows availability has not yet been announced, but 2026 is expected to bring updates on that front.

The Bottom Line

Arc was one of the most innovative browsers ever built and it proved that people want something better than Chrome. But innovation without adoption isn’t sustainable.

Dia takes everything Arc taught us and rebuilds it with AI at the core, a simpler interface and the full backing of a $610 million acquisition. If you loved Arc, Dia is where that story continues.

The browser wars are heating up again. Now is exactly the right time to pay attention.

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MindsDB Launches Anton: The Open-Source AI Agent That Thinks Like a Business Analyst

Sophia Lin
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0
MindsDB Launches Anton: The Open-Source AI Agent That Thinks Like a Business Analyst

If you’ve ever waited two days for a data report that answers a question you needed solved yesterday. You already understand the problem Anton is built to fix.

MindsDB, the AI data company backed by Benchmark, Mayfield and NVIDIA officially launched Anton on April 2, 2026. It’s an open-source autonomous business intelligence agent and it works differently from anything in the BI space right now.

What Makes Anton Different From Traditional BI Tools?

Anton
image source- anton

Most BI platforms give you a dashboard and expect you to figure out the rest. Anton flips that model entirely.

You type a question in plain English — something like What caused our Q1 revenue dip? and Anton takes over. It builds an analysis plan, writes and executes Python and SQL code, pulls data from your connected sources and delivers back tables, interactive charts and shareable dashboards with a written explanation of its reasoning.

No analyst queue. No waiting. No manual chart-building.

What makes this especially credible is the reasoning scratchpad — a step-by-step audit log of every decision Anton makes during an analysis. You don’t just get a result; you get a transparent, reproducible path that shows exactly how Anton reached its conclusion. That’s a meaningful differentiator in a space where AI black box outputs have made data teams nervous.

How Anton Learns Over Time

It isn’t a one-and-done tool. It’s built with a brain-inspired memory system that runs a consolidation pass after every session — extracting patterns, storing learnings and indexing them as searchable knowledge.

Over time, it picks up your business logic, your preferred KPIs, your naming conventions and your team’s analytical style. The more you use it, the more accurate and faster it becomes without any manual configuration.

This is where this ai starts to feel less like software and more like a junior analyst who actually pays attention.

Is Anton Secure Enough for Business Data?

This is the question most teams will ask first, and MindsDB has a clear answer.

Credentials are stored in an encrypted local vault that is completely isolated from the LLMs Anton uses for reasoning. All generated code runs inside constrained sandboxes meaning the model never has unrestricted access to your environment. Anton supports both Anthropic Claude and OpenAI models through a provider-abstraction layer, so you’re not locked into a single AI provider.

For enterprise deployments, the Minds Enterprise managed platform adds governance controls, credential isolation, cost management, audit trails and support for VPC, on-premises and private cloud environments. That makes it a realistic option even for finance, healthcare and other regulated industries.

Who Should Be Paying Attention to Anton?

It is built for teams across five key areas:

  • Revenue and RevOps — spotting pipeline gaps, stalled deals and conversion drops in real time
  • Finance — breaking down P&L performance with predictive forecasting models
  • Executive reporting — instant KPI drill-downs with plain-language explanations
  • Operations and supply chain — catching inefficiencies and anomalies before they escalate
  • Product teams — embedding live, AI-generated analytics directly into apps

The pattern here is consistent: It targets every situation where business decisions are delayed because the right data isn’t surfaced fast enough.

How to Get Started With Anton

The open-source version is available now on GitHub at github.com/mindsdb/anton and installs with a single terminal command on macOS, Linux, or Windows. There’s no complex setup, no vendor lock-in and no subscription required to try it.

For teams that need managed infrastructure with enterprise-grade controls. Minds Enterprise is the hosted option with full support.

The Bigger Picture

The conversational analytics market is crowded, but most competitors are still layering chat interfaces on top of legacy dashboards. It is built from the ground up to plan, execute, learn, and act — not just respond.

The gap between asking a business question and getting a defensible, data-backed answer has always been the bottleneck in BI. It is a serious attempt to close it permanently.

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