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Project Glasswing: The Death Verdict for Open Source?

TheRabbitHole — Fri, 10 Apr 2026 09:06:08 +0000

On April 7, 2026, Anthropic announced Project Glasswing—a defensive cybersecurity initiative built around Claude Mythos Preview, a frontier AI model so capable at finding and exploiting vulnerabilities that Anthropic deems it too dangerous for general public release. Backed by $100 million in usage credits and a "coalition of the willing" including Amazon, Apple, Google, Microsoft, Nvidia, the Linux Foundation, CrowdStrike, Palo Alto Networks, and more, Project Glasswing aims to give defenders a head start before similar capabilities proliferate to adversarial actors.

The announcement arrived during a remarkable week for Anthropic: the company disclosed $30 billion in annualized revenue (tripling in months), sealed a multi-gigawatt compute deal with Google and Broadcom, and faces potential IPO considerations. This timing raises immediate questions about whether Glasswing represents a watershed moment for cybersecurity, a strategic business move, or both.

What follows is a deep investigation drawing on Anthropic's own documentation, independent press analysis, technical community response, and security expert perspectives to evaluate Project Glasswing—the claims, the risks, the business strategy, and what it means for the future of digital security.

The Capabilities: Something Remarkable, or Marketing Hyperbole?

What Anthropic Claims

According to Anthropic's comprehensive technical evaluation, Claude Mythos Preview demonstrates:

Autonomous discovery of thousands of zero-day vulnerabilities in every major operating system and web browser
Ability to develop working exploits without human intervention—in one case chaining together four distinct vulnerabilities to escape browser sandboxes
Spectacular benchmark results: 83.1% on CyberGym versus 66.6% for Claude Opus 4.6, and 93.9% on SWE-bench Verified

Particularly striking are specific examples:

A 27-year-old vulnerability in OpenBSD—a security-focused OS—that allowed remote crash by mere connection
A 16-year-old bug in FFmpeg's H.264 codec, surviving five million automated fuzzing attempts
Autonomous local privilege escalation exploits on Linux by chaining multiple vulnerabilities

External Verification

FFmpeg maintainers have confirmed patches were submitted noting they "appear to be written by humans." Greg Kroah-Hartman, the Linux stable maintainer, has publicly stated: "Months ago, we were getting 'AI slop'... Something happened a month ago, and the world switched. Now we have real reports." Security teams across major open source projects report the same shift.

Forbes analyst Paulo Carvão notes that the evidence is "difficult to dismiss" given that Mythos can "chain together vulnerabilities that individually appear benign but collectively yield complete system compromise."

The Skeptical Community Response

On Hacker News, responses range from excitement about genuine advancement to bitter skepticism about relentless "doomer" marketing. One security professional noted they've already had success using existing models: "I've had these successes without scaffolding or really anything past Claude CLI and a small prompt as well? So like I'm in a weird place where this was already happening and Mythos is being sold like it wasn't good before?"

Others point out that we've heard dramatic breakthrough claims before. Anthropic's own CEO previously claimed 90% of code would be written by LLMs within 3-6 months—a timeline clearly not met. There's fatigue with each iteration being framed as world-endingly powerful.

Critical Assessment

This appears to be a genuine capability leap, not pure marketing. The technical documentation demonstrates stepwise exploit development that goes well beyond what was previously possible with autonomous AI. The 4% to 85% increase in Firefox exploit success rate (per Anthropic's internal comparisons between Opus 4.6 and Mythos) is substantial.

However, the implications are where hype and reality diverge. The capability is real. Whether it necessitates the dramatic response Anthropic has mounted—and whether Anthropic is the appropriate custodian—is less clear.

The Strategy: Controlled Release or Market Creation?

Anthropic's Stated Rationale

Anthropic makes a straightforward argument: Frontier AI cybersecurity capabilities are approaching (or have reached) a level that could fundamentally alter the security landscape. By limiting Mythos Preview to vetted defensive partners, they give defenders time to harden systems before similar capabilities become broadly available to adversaries.

This is framed as responsible AI governance—a model considered "too dangerous to release publicly" being deployed exclusively for defensive purposes.

Business and Competitive Dimensions

Forbes identifies five factors driving the invite-only rollout:

Real capability jump (as discussed)
Responsible AI governance positioning
Strategic marketing through scarcity—a narrative that generates enormous press
Capacity constraints—Anthropic is throttling usage; the model is compute-intensive
Premium pricing—$25/$125 per million input/output tokens (versus $5/$25 for Opus), positioning Mythos as a luxury security product

VentureBeat adds crucial context: The same day Glasswing launched, Anthropic disclosed $30B in revenue and sealed the Google-Broadcom compute deal. The timing intersects with IPO speculation. A "high-profile, government-adjacent cybersecurity initiative with blue-chip partners is exactly the kind of program that burnishes an IPO narrative."

Who Actually Gains Access?

The coalition structure creates an interesting dynamic. Tech competitors (Google vs. Microsoft) are both included. Smaller organizations and open-source maintainers are granted access via programs like "Claude for Open Source," with $4M in direct donations to open-source security organizations.

But critics note this creates new forms of exclusion. As one Hacker News commenter observed: "The fact that you won't be able to produce secure software without access to one of these models. Good for them $."

Whether the goal is truly defense for all, or defense for those who can afford/partner with Anthropic, is genuinely unclear.

The Risks: Defense, Offense, and the Zero-Day Explosion

The Core Paradox

The fundamental challenge Mythos presents is that the same capabilities used by defenders to find and fix vulnerabilities can be used by attackers to find and exploit them. Anthropic acknowledges this explicitly but argues that "the advantage will belong to the side that can get the most out of these tools."

In the short term, Anthropic warns, attackers who gain access to similar capabilities first could have a decisive advantage. In the long term, they expect defenders to prevail due to their ability to direct more resources and fix bugs before code ships.

The "transitional period" could be tumultuous.

What Happens When Adversaries Get Similar Models?

Malware News reports serious concern within the intelligence community. Analysts are "casually chatting" about the Mythos release. Multiple officials note that U.S. agencies both defend networks and conduct offensive operations—and stockpile zero-days for future use.

Hayden Smith of Hunted Labs calls the news "scary and ominous" because the offensive potential is unclear. "Even with deep vetting, the odds of Mythos flowing into the wrong hands is barely a hypothetical given the landscape of current attacks on the open source ecosystem."

The concern isn't just state actors. As one executive at a cyber investment firm asked: "How is anyone supposed to defend against all of this at once?"

The Patching Problem

Perhaps the most overlooked risk is the downstream impact of discovering thousands of vulnerabilities simultaneously. As Anthropic itself notes in its Red Team blog, "over 99% of the vulnerabilities we've found have not yet been patched."

Flooding maintainers—many of whom are unpaid volunteers—with critical vulnerabilities at scale could overwhelm the very processes needed to fix them. Anthropic has built a triage pipeline to manually validate reports before submission, but bottlenecks seem inevitable.

The 45-day coordinated disclosure window assumes maintainers can produce, test, and ship complex patches within that time—a presumption that may not hold for kernel-level vulnerabilities in critical systems.

Geopolitical Implications: AI as an Arms Race Component

The U.S. Government Relationship

Morgan Adamski, former executive director at U.S. Cyber Command, notes that "there's obviously a huge potential there from an adversarial perspective" for offensive use. She highlights an "equity conversation": if the U.S. exploits something in an adversarial network, it must also defend against that same vulnerability in its own infrastructure.

Anthropic has briefed senior officials across the U.S. government on Mythos's capabilities, including both offensive and defensive applications. This comes after contentious disputes with the Pentagon over military uses of Claude, which saw Anthropic designated a "supply chain risk" before securing a preliminary injunction.

Leah Siskind of the Foundation for Defense of Democracies argues: "The government 'needs to make amends with Anthropic and help them and Glasswing members maintain the American lead on AI by preventing Chinese model theft.'"

The International Dimension

As Project Glasswing proceeds, other nations (particularly China, Russia, and U.S. adversaries) will almost certainly develop or acquire similar capabilities. Mythos-level models will eventually proliferate. The question isn't whether, but when—and whether the defensive advantages gained during the controlled rollout period will be durable.

One concern: By making Mythos capabilities known while restricting access, Anthropic may have inadvertently created a roadmap for other AI labs to target. The technical specifications described in the system card provide a benchmark to aim for.

Trust and Irony: The Custodian Problem

Anthropic's Security Track Record

It is rich irony that Anthropic—asking governments and Fortune 500 companies to trust it with a model capable of autonomously exploiting Linux kernels—has suffered notable security lapses:

A draft Mythos blog post was left in an unsecured, publicly searchable data store in March 2026, exposing roughly 3,000 internal assets
For approximately three hours in March 2026, anyone running npm install on Claude Code pulled down 512,000 lines of Anthropic's source code due to a packaging error

Nicholas Carlini of Anthropic distinguishes these as "human errors in publishing tooling" rather than breaches of core security architecture—accurate as far as it goes, but a distinction that may not reassure stakeholders.

The Boy Who Cried Wolf?

There is legitimate concern about alarm fatigue. As Hacker News commenters note, every model is framed as revolutionizing everything, predicting doom if mishandled. When the next genuinely concerning capability arrives, will security practitioners—and the public—still be listening?

Conversely, as others pointed out: "Tuning out completely because of the existence of false positives is not a good choice." The villagers may tire of the boy crying wolf, but wolves do eventually arrive.

Pros and Cons: A Critical Summary

Pros

Aspect	Assessment
Genuine capability improvement	The demonstrated ability to autonomously find and chain vulnerabilities is a real step forward
Proactive defense	Finding bugs before adversaries do is fundamentally sound strategy
Open-source support	$4M in donations to OSS security addresses real asymmetries in resources
Responsible disclosure pipeline	Triage and human validation demonstrate awareness of maintenance bottlenecks
Transparency	Detailed technical documentation with cryptographic commitments shows seriousness
Coalition approach	Bringing competitors together on security reduces fragmentation

Cons

Aspect	Assessment
Exclusionary access	Creates dependency on Anthropic; smaller actors may be left behind
FOMO and coercion	Organizations may join not out of belief but fear of seeming negligent
Overwhelmed maintainers	Even with triage, the scale of findings risks swamping patching capacity
Verification limited	Access restrictions make independent verification of claims difficult
Business opportunism	Timing with IPO and revenue milestones suggests mixed motives
Geopolitical escalation	Demonstrating capabilities may accelerate adversarial AI development
Trust issues	Anthropic's security lapses undermine its credibility as gatekeeper

Critical Opinions from Multiple Perspectives

The Security Community

On Hacker News, security professionals express a range of views:

Skeptical: "This looks more like another lobby group...The 'urgency' is very likely mostly appreciated to drive policy."
Concerned: "How is anyone supposed to defend against all of this at once?"
Measured: "I side with you but on the other hand: this is how it works to get attention by those who aren't affiliated with computer science and AI."
Optimistic: "At launch, a technology is considered dangerous for being too powerful. 3 months later, you are an absolute idiot to still be using that useless model."

Greg Kroah-Hartman's quote—about the "world switched" from AI slop to real reports—stands out as evidence from a respected figure in Linux development.

Industry Analysts

Paulo Carvão at Forbes takes a nuanced view, noting both genuine capability and strategic positioning: "This announcement cannot be understood in isolation" from Anthropic's revenue growth and compute deals. The restricted rollout serves multiple purposes.

Michael Nuñez at VentureBeat focuses on the fundamental wager: "Anthropic is, in essence, betting that transparency can outrun proliferation."

Intelligence and Government Concerns

Morgan Adamski emphasizes the offense-defense equivalence: "If cyberintelligence analysts find a novel vulnerability in an enemy computer network, it's possible a U.S. system might have the same vulnerability, too."

The intelligence community's "casual" discussions and serious concern about adversarial acquisition mirror the stakes: this isn't just a cybersecurity issue; it's a national security issue.

The Open-Source Perspective

Jim Zemlin, CEO of the Linux Foundation, provides perhaps the most compelling endorsement: "In the past, security expertise has been a luxury reserved for organizations with large security teams. Open-source maintainers—whose software underpins much of the world's critical infrastructure—have historically been left to figure out security on their own." Project Glasswing, he says, "offers a credible path to changing that equation."

This gets at a real problem: the asymmetry between well-resourced corporations and the volunteer-maintained projects that form software's foundation.

Conclusion: A Necessary Step, But A Flawed One?

Project Glasswing represents a genuinely significant moment in AI development. The technical capabilities of Claude Mythos Preview appear real enough that Anthropic—not a company known for understatement—is willing to frame them as too dangerous for public release. The decision to limit access to defensive partners and invest in open-source security is, in principle, defensible.

But the initiative is also deeply problematic:

It concentrates power in Anthropic's hands during a transition period that will be contested globally
It markets through scarcity, creating artificial urgency that serves business interests
It may overwhelm the very maintenance processes needed to address discovered vulnerabilities
It invites escalation, as other labs rush to match or exceed demonstrated capabilities
It suffers from trust deficits, given Anthropic's own security history and the incentives of a company on an IPO trajectory

The core question—whether Project Glasswing genuinely makes the world more secure, or merely reshapes advantage within existing power structures—has no clear answer yet. The only certainty is that the age of AI-augmented cyberconflict has begun in earnest. The glasswing's transparent wings hide vulnerabilities well. But in seeking to reveal those vulnerabilities to defenders first, Anthropic may have revealed something else: just how quickly the ground beneath cybersecurity's feet is shifting.

In the coming months—before the next frontier lab announces its own game-changing model, before adversarial access reaches Mythos-equivalent levels, before the inevitable disclosure of vulnerabilities that even Anthropic cannot contain—we will learn whether controlled releases like Project Glasswing can genuinely preserve a defensive advantage, or whether the fundamental symmetries of offense and defense make this a game of diminishing returns.

The wolf may or may not have arrived. But when it does, the villages that invested in defenses during the calm will have a better chance. Whether Anthropic should be the one selling those defenses is the question that remains.

Beyond OpenClaw: The Rise of the Lightweight AI Agent Ecosystem in 2026

TheRabbitHole — Fri, 06 Mar 2026 10:43:54 +0000

OpenClaw (originally Clawdbot) has long been the dominant force in autonomous AI agents, boasting over 267,000 GitHub stars. But as its codebase has ballooned to over 430,000 lines, developers have begun to voice concerns over its massive resource footprint and security vulnerabilities.

In response, a "small-is-beautiful" revolution has taken over GitHub. Developers are flocking to lightweight, transparent alternatives that prioritize security, auditability, and efficiency.

If you are looking for projects similar to NanoClaw, here is your comprehensive guide to the ecosystem of lightweight alternatives.

Top Open-Source Lightweight Alternatives

These projects share a common philosophy: a smaller codebase means better auditability and lower resource usage.

1. NanoClaw

Language: TypeScript (Node.js)
GitHub Stars: ~19,500
Focus: Security-First & Container Isolation
The Pitch: NanoClaw is the go-to choice for security-conscious developers. Unlike the original OpenClaw, which often runs in a single process with shared memory, NanoClaw forces OS-level container isolation (e.g., Apple Containers on macOS). This ensures that even if an agent goes rogue, it cannot access your host machine's filesystem or sensitive .env credentials. It integrates seamlessly with the Claude Code ecosystem.

2. Nanobot (University of Hong Kong)

Language: Python
GitHub Stars: ~29,400
Focus: Extreme Transparency & Simplicity
The Pitch: If your goal is to learn or customize, Nanobot is unmatched. It is roughly 4,000 lines of Python—about 99% smaller than OpenClaw. Despite its tiny size, it packs in persistent memory, web search, and integrations for Telegram and WhatsApp.

3. ZeroClaw

Language: Rust
GitHub Stars: ~23,700
Focus: High Performance & Safety
The Pitch: For the production environment, ZeroClaw offers "Claw done right." It compiles down to a 3.4 MB binary and uses less than 5 MB of RAM at runtime. Its standout feature is being "secure-by-default" with strict workspace scoping for filesystems.

4. NullClaw

Language: Zig
GitHub Stars: ~5,480
Focus: Ultra-Minimalist Runtime
The Pitch: NullClaw is extreme minimalism incarnate. It produces a static binary of only ~678 KB that boots in milliseconds. It is the ideal candidate for edge devices and IoT scenarios where every byte counts.

5. PicoClaw

Language: Go
GitHub Stars: ~12,000+
Focus: Embedded Hardware & IoT
The Pitch: PicoClaw is designed to run on cheap hardware. It can operate on $10 RISC-V boards with less than 10 MB of RAM. It also includes free voice transcription via Groq Whisper, making it a powerhouse for hobbyists working on embedded projects.

Summary Comparison

Tool	Language	Footprint	Best For
NanoClaw	Node.js	Small	Security-first / Container isolation
Nanobot	Python	~4K lines	Learning / Simple customization
ZeroClaw	Rust	<5 MB RAM	High performance / Safety
NullClaw	Zig	678 KB	Extreme edge/IoT minimalism
PicoClaw	Go	<10 MB RAM	Cheap embedded hardware

Specialized & Enterprise Alternatives

While the projects above focus on being lightweight, other alternatives are targeting specific enterprise niches:

memU: Focuses on "proactive" assistance using a Hierarchical Knowledge Graph for superior long-term memory.
Moltworker: A serverless version of OpenClaw hosted on Cloudflare Workers, offering sandboxed execution without local machine access.
Adopt AI: An enterprise-grade platform that automates API discovery and action generation for complex corporate workflows.
TinyClaw: A multi-agent system that coordinates specialized agents (coder, researcher, etc.) in parallel via a live terminal dashboard.

Which Projects Are Rising the Fastest?

As of March 2026, the growth charts show a clear divide between the established educational tools and the new production-ready contenders.

🚀 PicoClaw (The Viral Leader): Gained over 12,000 stars in its first week. Its ability to run on $10 hardware has captivated the maker community.
📈 ZeroClaw (The Pro Choice): Seeing a surge in professional adoption. It is currently the preferred choice for developers wanting a robust, "agentic OS" workflow.
🛡️ NanoClaw (The Security Pick): Growing rapidly among security circles, particularly due to its recent "Agent Swarms" update and compatibility with Claude Code.

How to Choose?

Choose Nanobot if you want to read the code and understand how agents work.
Choose ZeroClaw if you need speed and memory safety for a production app.
Choose NanoClaw if you are handling sensitive data and need strict container isolation.
Choose PicoClaw if you want to build AI into physical devices on a budget.

The era of the "bloated agent" is ending. With tools like these, the future of autonomous AI is fast, secure, and accessible.

Agents Can Now Clone Themselves and Do Crazy Things (Part I: Deep Stock Analysis)

TheRabbitHole — Mon, 26 Jan 2026 10:17:48 +0000

Most chatbots, such as ChatGPT and Claude, are becoming more powerful every day. They are incorporating more tools, characters and features, such as Canvas or Artifacts, to improve usability. However, especially if you are a heavy user of AI (especially as a non-coder), the limitations are the same: the more data and the more complex the tasks, the less AI becomes usable.

It becomes lazy and takes shortcuts.

It hallucinates. It forgets things. The quality degrades massively, and worst of all, you still pay for it.

Most of these issues are known limitations that happen because of one of the most limiting factors of AI: the context window. Think of it as the AI's limited working memory: the more data it contains, the more overwhelmed the AI becomes while still trying to please you. The result is a pure waste of time and money.

The Solution That Changes Everything

There have been a lot of advancements in this area trying to overcome these technical limitations, such as plugging in memories, but one incredibly powerful solution is multi-agency.

The AI breaks down tasks it has never seen before using its reasoning capabilities and sends them to other AIs (so-called subagents) to complete. Then it aggregates the results and answers the user's request.

In this approach, the so-called sub-agent starts with a fresh memory. It doesn't need to know the entire context; it just needs to know the subtask at hand. It executes the task, delivers the results and disappears. Any further subtasks start with a new LLM. This core difference to having one large LLM trying to do everything by itself changes the entire game.

Handling much more complex tasks becomes possible. You get much less hallucination and much higher quality. Think of those subagents focusing on one smaller task; they can perform much better than trying to handle a huge task all at once. And if you have parallelisation, the end-to-end experience can be much faster than single processing, though this also depends on the tooling of the multi-agent solution.

The Tools You Can Use Right Now

If you follow the news, you might have heard about Claude Cowork. Built on top of a framework developed by Anthropic a few months ago, called Agent SDK, Claude Cowork can process highly complex tasks end-to-end using a high-reasoning, multi-agent approach.

It develops a well-thought-out plan for accomplishing a given complex task from start to finish. It spawns multiple agents ad hoc (think of it as a scalable team on demand). It extends code in a sandbox environment, giving users the full power of coding without requiring any prior knowledge (e.g. reading and editing files, calling APIs, and much more).

This tool is incredibly powerful, but expensive, though worth the investment if you consider the ROI.

If you are reluctant to pay a monthly subscription fee of $100 to $200, you can also use the framework with code, or you can use Cherry Studio, an open-source chatbot that integrates this framework.

A Real World Example: Deep Analysis of Microsoft's 2025 Annual Report

This technology can be used to solve a variety of complex tasks, including those that require the use of tools. Imagine presenting a dense financial report to different experts (financial gurus, strategists, etc.) to obtain a comprehensive view of the results.

The coordinating AI (the one you are talking to in the chat) decides ad hoc how many agents to use, how to prompt them, and so on. You don't need any prior configuration. That's the real beauty of this amazing technology.

The process works like this: First, the system reads the contents of the report, then sends subtasks to multiple expert subagents. Each of these subtasks is a subagent with its own memory and tools. After a minute or so, you have a detailed analysis of the final report compiled from five different angles.

Cost Considerations

You might be wondering how much this will cost you. For a dense report with millions of tokens processed, you're looking at roughly $2.50 to $3.00 USD using Haiku 4.5, especially when cached tokens reduce the total cost significantly. If there's a lot at stake for you, it's more than worth every penny.

Getting Started in Three Steps

Try it yourself with Cherry Studio. Install Cherry Studio from the official repository, add the API key for Anthropic, and click 'Add Agent' on the right. Then select the model and create a scratch area. That's it.

Now you can start chatting with the agent and let it free you from those painful, boring tasks.

Read the full deep dive on airabbit.blog: https://airabbit.blog/agents-can-now-clone-themselves-and-do-crazy-things-part-i-deep-stock-analysis/

Is The Future of AI is On-Demand?

TheRabbitHole — Sat, 24 Jan 2026 19:11:21 +0000

Recently, a friend of mine who has no affiliation with IT whatsoever approached me with great excitement about an app he had developed overnight. He built the whole thing on his phone. I was baffled, though not surprised. These days, almost anything is possible — or at least, we like to think so.

This new reality makes technology accessible to almost everyone. All you need is an idea, a phone and a subscription for a month or so, and you're good to go, right?

Forgetting for a moment the 'crimes' that laypeople are committing regarding day-two operations (patching, security, etc.), the world is already flooded with apps. Everyone has their own business model, subscription process and requirements for signing up.

For consumers, this is becoming a nightmare.

Sharing your personal data with each and every one of them.

Paying everyone a subscription.

And so on.

I used to have lots of these apps and subscriptions one or two years ago.

Presentation AI
AI chatbots (Claude, ChatGPT, etc.).
Canva
AI video and image generators (Runway, etc.).
Freepik

And many more.

And that’s just for AI!

I have started to cancel a lot of subscriptions, including ChatGPT and Claude. I have started switching to platforms that aggregate all of these solutions in one place, with one account and one subscription — and that’s it!

This has shown me that I don't actually need to pay for a monthly or yearly subscription just to generate ads (like AdCreative) or flyers (like Canva). I do a lot, but I don't need a permanent subscription for that.

How Aggregation Platforms Work

Aggregation platforms such as Poe and Apify — and, I believe, ChatGPT in the future — bring together all the services and apps available. Think of it as a 'pay once, use all' model, with the amount depending on the subscription plan.

This is different from Amazon, where you just have a directory and pay each one individually (this is what we have now).

Apify is one amazing platform that has proven how powerful this business model is.

When you subscribe to Apify, you get access to around 5,000 "actors", most of which have flexible pricing options, such as paying per output result or even per call.

For example, I pay $50 per month and can use:

LinkedIn actors to scrape LinkedIn;
Reddit actors to scrape Reddit.
data analytics actors, such as Semrush, for in-depth analysis.
and many more

With pay-per-use, I don't have to pay for the Reddit API or a Semrush subscription. You get my point.

The Future of Aggregation

Now, think of this same concept with ChatGPT Store. We could have these giant platforms hosting thousands of AI services for everything:

Creative writing
Generating presentations
Generating images
Or even entire videos or books.

And all on a pay-per-use basis. This is technically already possible but still at a very early stage.

The Caveat

One could think of monopoly platforms such as Amazon, and of course, serious concerns arise with regard to control, power and security. However, we must also consider:

How much power do they exert?
How do they monetise developers?
The policy: what does and doesn't match their strategy.
A single point of failure.

In an ideal world, there would be multiple platforms that aggregate services, eliminating the need for multiple registrations and payments, and saving time and money on testing things that we rarely use — and even worse, things that don’t fulfil their promises, which we often only realise after paying a hefty subscription.

Stop Trying to Pick the 'Best' LLM. Let Them Answer Together (For Under a Dime)

TheRabbitHole — Sat, 24 Jan 2026 16:48:22 +0000

We've all been there. You ask ChatGPT for architectural advice, and it gives you a confident answer. But something nags at you — is this actually the best approach, or just the first one the model latched onto?

Single models have blind spots. They're trained on specific datasets, optimized for certain response patterns, and prone to confident-but-wrong answers. Getting a second opinion from a different model helps, but manually copying prompts between interfaces is tedious.

What if you could query multiple top-tier models simultaneously and see where they agree, disagree, or bring up angles you hadn't considered?

That's exactly what Super AI Bench does. It's an MCP (Model Context Protocol) server that acts as your AI consensus engine, automatically querying the smartest available models and synthesizing their responses.

The Simple Idea: AI as a Panel, Not an Oracle

Instead of treating AI as a single expert, think of it as a panel of specialists. Each model has different training data, architecture, and "experience":

Claude tends toward careful, nuanced analysis with strong ethical considerations
GPT-4 excels at structured reasoning and technical implementation details
Gemini often brings in creative angles and cross-domain connections
Mistral might prioritize efficiency and practical constraints

When they converge on an answer, you can be more confident. When they diverge, you see the complexity instead of getting a false sense of certainty.

Real Example: Debugging a Production Issue

Let's say you're troubleshooting a memory leak. Here's what a multi-model consensus looks like in practice:

Your prompt:

"Node.js app memory grows 2% hourly. Heap dumps show string accumulation. 
Using Express, Redis, and Winston. Where should I look?"

Consensus results:

{
  "models_queried": 5,
  "response_time": "8.3s",
  "consensus": {
    "high_confidence": [
      "Check Winston transport configuration",
      "Review Redis connection string handling",
      "Look for unclosed response streams"
    ],
    "divergent_opinions": {
      "claude_3.5": "Mentioned event listener leaks in error handlers specifically",
      "gpt_4": "Suggested checking for large request/response logging",
      "gemini_1.5": "Flagged potential issues with custom formatters retaining references"
    },
    "unique_insights": [
      "One model spotted that your Redis retry strategy might be buffering commands",
      "Another noted that Winston's FileTransport with high logging levels can accumulate"
    ]
  }
}

Instead of one model's best guess, you get a prioritized checklist and discover edge cases you might have missed.

Strategic Example: Business Decision Making

Imagine you're a product manager deciding whether to pivot your SaaS platform toward AI features or double down on core functionality.

This isn't a technical question. It's strategic, involves market assumptions, financial risk, and competitive positioning. A single AI model will give you one perspective with high confidence. But what are you missing?

With Super AI Bench, you send one prompt: "Our SaaS has 5K users, strong retention, but slower feature velocity than competitors. Should we pivot to add AI features or strengthen core product? Consider: market timing, engineering cost, user retention risk, competitive threat."

What you get back:

Claude focuses on user risk and thoughtful long-term strategy ("Don't chase trends; validate demand first")
GPT-4 brings structured business analysis ("Calculate CAC impact on both paths; model the revenue upside")
Gemini surfaces market dynamics you hadn't considered ("AI features become table stakes in 12 months for your category")
Mistral emphasizes resource constraints ("You don't have the engineering bandwidth for both")

Instead of one confident answer, you see the trade-offs clearly. You discover that the real decision isn't "pivot or not" — it's "whether you have the team capacity to do it well." That insight alone might save you six months of wasted effort.

This is where consensus becomes valuable: not because the models are always right, but because you see the problem from multiple angles instead of getting a false sense of certainty from a single perspective.

More Affordable Than You Might Think

Running multiple models sounds expensive, but for many use cases, the cost is surprisingly low. Most queries cost less than a penny, and even complex analyses rarely exceed a few cents.

Here are a few real examples:

Quick technical question: 3 models responded in under 1 second total, cost was less than $0.01
Detailed code review: 3 models took 7-34 seconds, cost was $0.01-$0.02
Complex architecture discussion: Multiple models provided detailed responses for less than $0.02 total

When you consider the cost of a wrong decision or missed bug, spending a few cents to get multiple perspectives is a pragmatic investment.

When This Actually Helps

✅ Good use cases:

High-stakes decisions where blind spots are costly (architecture, security)
Creative blocks when you need fresh perspectives (marketing campaigns, product features)
Risk assessment to surface concerns you hadn't considered
Learning complex topics by seeing different explanation styles
Fact-checking controversial claims by checking for consensus

❌ Don't bother when:

You need a quick, simple answer ("What's the Python string length function?")
The task is deterministic (math calculations, code syntax)
You're on a tight budget (5 models = 5x the API costs)
You already have deep expertise in the domain

The Honest Limitations

This isn't magic. It's pattern matching at scale.

Cost: Running 5 top-tier models isn't cheap. Use it for important questions, not every query.
Speed: You'll wait 5-10 seconds for all responses. It's not for real-time applications.
Agreement ≠ Truth: Models can all be wrong in the same direction. They share some training data and architectural biases.
Divergence ≠ Uselessness: Sometimes the outlier model catches something critical. The "consensus" is just a starting point for your own judgment.

Not Just Another Multi-Model Chatbot

You might be thinking: "Can't I just use one of those open-source chatbots that let me select multiple models and send them the same prompt?"

This is fundamentally different.

Open-source multi-model chatbots are static - You have to manually choose which models to query, copy your prompt to each one, and then manually compare the responses yourself. It's a tedious, repetitive process that doesn't scale.

Super AI Bench is dynamic and AI-driven - The AI assistant frames your question, automatically determines which models are most suitable based on live benchmarks, sends the prompt to them in parallel, and aggregates the results into a coherent summary. All without any interaction from you after the initial prompt.

The difference is night and day:

Before: "Let me check 3 different models manually..."
After: "Hey AI, what's the best approach here?" (30 seconds, fully automated)

This isn't just about querying multiple models - it's about intelligent orchestration that removes the friction entirely.

Setup in 30 Seconds

Getting started is simpler than you might think. You only need two accounts:

Apify account - Free tier available, and login uses OAuth (no password needed)
Replicate account - For accessing the AI models, just grab your API key

That's it. No complex configuration, no infrastructure to manage.

Add this to your MCP settings:

{
  "mcpServers": {
    "super-ai-bench": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://flamboyant-leaf--super-ai-bench-mcp.apify.actor/mcp?replicateApiKey=<REPLICATE_API_KEY>"
      ]
    }
  }
}

Just replace <REPLICATE_API_KEY> with your actual key. Apify handles authentication automatically through OAuth when you first use the actor.

From that point forward, simply select the "Super AI Bench" MCP in your AI assistant, frame your question, and let it query multiple models and summarize the responses for you. The actor manages all the parallel calls, error handling, and response formatting.

See the README for more configuration options and advanced usage patterns.

Testing MCP Servers like a Pro using MCPJam Inspector

TheRabbitHole — Wed, 21 Jan 2026 11:37:07 +0000

Building and testing MCP (Model Context Protocol) servers is frustrating without the right tools. Most developers waste hours switching between different environments—writing code, then switching to clients like Cursor or Claude Desktop just to test a simple function call, then back to the IDE to debug issues. You're constantly guessing what's wrong when tools fail: Is it the MCP protocol implementation? The connection parameters? The tool definition? MCPJam Inspector solves this by giving you a dedicated, visual workspace for testing, debugging, and validating MCP servers without ever leaving your development flow. It's the difference between fumbling in the dark and having X-ray vision into your MCP implementation.

What is MCPJam Inspector?

MCPJam Inspector is a local-first developer tool for testing, debugging, and inspecting Model Context Protocol (MCP) servers and ChatGPT/OpenAI apps. Think of it as "Postman for MCP"—a visual interface that lets you explore, test, and debug MCP servers without needing to deploy them or connect through production clients.

Key Features

Visual Server Management: Connect to MCP servers via STDIO, HTTP, or SSE protocols
Tool Testing: Manually invoke and test MCP tools with custom parameters
Resource Inspection: Browse and fetch resources exposed by MCP servers
Prompt Templates: Test and use prompt templates with slash commands
LLM Playground: Simulate how your MCP server performs with various LLMs (OpenAI, Claude, Ollama, etc.)
Real-time Logging: View all JSON-RPC messages, requests, responses, and errors
OAuth Debugging: Test and debug OAuth flows for authenticated servers
Chat Interface: Interact with your MCP server conversationally

What Can You Use It For?

Development: Build and test MCP-based tools locally without switching to clients like Cursor or Claude Desktop
QA & Debugging: Validate tool definitions, prompt templates, and resource endpoints against the MCP specification
Experimentation: Test your MCP server with different LLM models to see how it behaves in various contexts
Learning: Understand how MCP servers work by inspecting the protocol messages in real-time
Integration Testing: Verify that your MCP server works correctly before deploying to production

About This Tutorial

This tutorial demonstrates how to use MCPJam Inspector to add and test MCP servers. We use the Tavily MCP server as an example, but the same process works for any MCP server:

Custom MCP servers you've built
Third-party MCP servers (GitHub, Slack, Notion, etc.)
Local MCP servers running on your machine
Remote MCP servers via HTTP/SSE

The steps are identical—just replace the server URL and configuration with your own MCP server details.

Overview

This tutorial walks you through using MCPJam Inspector to:

Add an MCP server (using Tavily as an example)
Connect via HTTP/SSE
View available tools from the server
Test the tools with custom parameters

Prerequisites

MCPJam Inspector running at http://127.0.0.1:6274
An MCP server to connect to (we'll use Tavily as an example - get an API key from Tavily's website if following along)

Step 1: Open MCPJam Inspector

Navigate to http://127.0.0.1:6274 in your browser. You'll see the main dashboard with no servers connected.

Step 2: Click "Add Server"

Click the "Add Server" button in the top right corner of the MCP Servers section.

Step 3: Select HTTP Connection Type

The dialog opens with STDIO selected by default. Click the Connection Type dropdown and select "HTTP".

After selecting HTTP, the form changes to show HTTP-specific fields:

Server Name: Enter a name for your server
URL: Enter the Tavily MCP server URL
Authentication: Configure if needed
Custom Headers: Add any custom headers

Server Name: Enter a name for your server (we used tavily as an example)
URL: Enter your MCP server URL. For the Tavily example:

   https://mcp.tavily.com/mcp/?tavilyApiKey=YOUR_API_KEY

Note: Replace YOUR_API_KEY with your actual API key. For other MCP servers, use their respective connection URLs.

Click the "Add Server" button at the bottom of the dialog. The server will connect automatically.

You can see:

Server name: tavily (or whatever you named it)
Connection type: HTTP/SSE
Status: Connected
Server version: v2.14.2

Click on "Tools" in the left sidebar to see all available tools from your MCP server.

In our example with Tavily, we see 4 tools:

tavily_search - Search the web for real-time information
tavily_extract - Extract content from specific web pages
tavily_crawl - Crawl multiple pages from a website
tavily_map - Map and discover website structure

Different MCP servers will expose different tools based on their functionality.

Step 8: Test a Tool

Click on any tool from your MCP server to open its configuration form. In our example, we'll test "tavily_search".

The form shows all available parameters for the selected tool. Each MCP server's tools will have different parameters based on their functionality.

Step 9: Enter Parameters

Fill in the required parameters. For the tavily_search example, enter a test query like: MCP protocol tutorial

Step 10: Execute the Tool

Click the "Execute" button to run the tool. The button will show "Running" while processing.

The results will appear in the Response section below, showing the tool's output in a structured format. The exact format depends on what the tool returns.

Next Steps

Explore More Tools

Try other tools from your MCP server
Test different parameter combinations
View the logs to see the JSON-RPC messages being exchanged

Use MCPJam Inspector's Advanced Features

Chat Interface: Interact with your MCP server conversationally using natural language
LLM Playground: Test how different LLMs (OpenAI, Claude, Ollama) use your MCP server's tools
Resource Templates: If available, explore prompt templates for standardized tool usage
Tracing: Monitor detailed request/response flows to understand how the MCP protocol works
Test Cases: Create and save test cases for automated testing of your MCP integrations

Happy Coding!

A Smarter Way to Find and Test AI Models for Your App using GPT + Super AI (MCP)

TheRabbitHole — Mon, 19 Jan 2026 16:26:09 +0000

Modern development tools have made building applications easier than ever. You can now launch a new app with a database, authentication, and other core features in minutes. The final piece of the puzzle, adding genuine intelligence with AI, however, introduces a new set of challenges.

Developers often face several key questions when integrating AI:

Which AI model provider should you choose?
How do you price your product to account for AI usage costs?
If you're using your own API key, how do you protect it from misuse and prevent unexpected expenses?

These questions become even more critical if you plan to offer a free trial or a free tier for your application. Without a proper strategy, you risk having your budget drained by overuse and users who don't intend to subscribe. While many solutions exist, one straightforward approach is to ship your product with a local AI that performs its specific task efficiently.

The Power of Local, Specialized AI

Amazing technologies are available that allow you to bundle a lightweight AI model directly with your application. This can be as simple as the snippet below, which creates a basic chatbot within a single HTML file.

Before you adopt this approach, there are two fundamental questions you need to answer:

What specific use case should your model excel at? Most developers know that smaller models are not generalists like the mega-models behind services like ChatGPT. Instead, they are fast, cheap, and lightweight specialists. Your use case might be document classification, language translation, text summarization, or another focused task.
Which model is the right one for that use case? After defining the task, you need to find a model that can perform it effectively.

The first requirement is a core part of any successful business plan. The second, however, can be a significant challenge when you have to choose from hundreds of available models. There are many benchmarking platforms like Hugging Face's LLM Leaderboard, LMSys's Chatbot Arena, and Artificial Analysis, plus countless online playgrounds to test individual models. But sifting through them all takes time.

Automating Model Discovery with AI

If you have a handful of use cases and need to iterate quickly, you can use AI an Super AI MCP to automate the discovery and testing process. Here’s how it works:

Configure an AI to access benchmark data. This gives your AI assistant the information it needs to compare models.
Configure the AI to access prediction platforms. This connects your AI to services that host a wide variety of models.
Provide your use cases in natural language. Let the AI find the most suitable models and run tests for you.

To make this work, you only need two key components:

Any chatbot that supports the Model Context Protocol (MCP), such as ChatGPT, Claude, and others.
A free account at Apify.com to access benchmark data using a specific MCP. (Requires an API key).
(Optional) A Replicate account if you want to run predictions. (Requires an API key).

You can then use a prompt like this:

Find the best 3 small models that can do this task and try them out on Replicate: 

--- my task 1 here 
--- my task 2 here 
etc..

Let’s walk through an example.

Prerequisites:

ChatGPT (or another chatbot with MCP support)
An Apify API key (a free account is sufficient)
A Replicate API key (this is a pay-per-use service)

Step-by-Step Guide to Automated Model Testing

Step 1: Configure the MCP Server

First, you need to connect your chatbot to the benchmark and prediction tools using an MCP server.

Start by adding a new MCP in your chatbot's settings.

You will need to provide the server URL.

Use the following URL, adding your Replicate API key at the end where indicated.

https://flamboyant-leaf--super-ai-bench-mcp.apify.actor/mcp?replicateApiKey=

Leave the OAuth section empty, as you will authenticate with Apify later. Click confirm to save.

That's it for the configuration.

Step 2: Find and Analyze Suitable Models

Now, let's try a simple example to find some high-value small models. Later, you can replace this with your own specific use cases.

In your chatbot, enter the following prompt:

Find the best small model

ChatGPT will now ask the benchmark tool for suitable models and sort them based on the request.

Here, it has found several models, including different versions of Llama, Qwen, and Phi, along with necessary data like size and cost.

The AI then provides a quick recommendation of which models to use.

Step 3: Test the Models on Replicate

This is useful, but the real power comes from seeing the models execute your use case. Here, we'll let the AI create and run a simple coding task.

Use the following prompt:

try them on replicate

The AI will first search for suitable models available on the Replicate platform. Note that not all models listed in benchmarks are on Replicate, but in this case, they are.

Now, we can run the test on all of them simultaneously.

You can see the jobs running in your Replicate dashboard, with details including creation date, duration, and more. Your AI also has access to this data.

https://replicate.com/predictions

After approximately one to two minutes, our use case has been tested across five different models, and we receive a detailed analysis directly from the AI.

Real-World Applications and Benefits

This was a very simple example. In a real-world scenario, you can:

Provide your own complex, specific use cases for testing.
Save the results for future comparison.
Evaluate new models as they are released without switching between different platforms.
Distribute complex tasks across multiple models to leverage their unique strengths.
And much more.

While all of these capabilities are valuable, the greatest benefit is the ability to quickly compare results from different models without subscribing to multiple services. As mentioned at the beginning of this post, this process makes it significantly easier to find small, efficient models that you can confidently ship with your products.

Appendix

Pure HTML/JS Chatbot (Snippet)

Open your Chrome browser and enable the on-device model at

chrome://flags/#optimization-guide-on-device-model

Then save this HTML file and just open it. The rest is self-explanatory.

<!doctype html>
<html lang="en">
<head>
  <meta charset="utf-8" />
  <meta name="viewport" content="width=device-width,initial-scale=1" />
  <title>Local LLM Chat (Browser)</title>
  <style>
    :root { color-scheme: dark; }
    body { margin: 0; font: 14px/1.4 system-ui, -apple-system, Segoe UI, Roboto, Arial; background:#0b0f14; color:#e6edf3; }
    .wrap { max-width: 980px; margin: 0 auto; padding: 16px; display:flex; flex-direction:column; gap:12px; height: 100vh; box-sizing:border-box; }
    .top { display:flex; gap:10px; align-items:center; flex-wrap:wrap; }
    .chip { padding:6px 10px; border:1px solid #223; border-radius:999px; background:#0f1621; }
    .status { opacity:.9; }
    .chat { flex:1; overflow:auto; border:1px solid #223; border-radius:12px; padding:12px; background:#0f1621; }
    .msg { margin: 0 0 10px 0; white-space:pre-wrap; }
    .msg .role { font-weight:700; }
    .msg.user .role { color:#7ee787; }
    .msg.ai .role { color:#79c0ff; }
    .row { display:flex; gap:10px; }
    input, select {
      padding:10px; border-radius:10px; border:1px solid #223;
      background:#0b0f14; color:#e6edf3;
    }
    #inp { flex:1; }
    button { padding:10px 12px; border-radius:10px; border:1px solid #223; background:#1f6feb; color:#fff; cursor:pointer; }
    button.secondary { background:#0f1621; }
    button:disabled { opacity:.5; cursor:not-allowed; }
    .small { font-size: 12px; opacity:.8; }
    .hide { display:none; }
  </style>
</head>
<body>
  <div class="wrap">
    <div class="top">
      <span class="chip">Transformers.js (browser local)</span>

      <label>
        Model:
        <select id="modelSelect">
          <option value="HuggingFaceTB/SmolLM2-135M-Instruct">SmolLM2-135M-Instruct (recommended)</option>
          <option value="HuggingFaceTB/SmolLM2-360M-Instruct">SmolLM2-360M-Instruct (bigger)</option>
          <option value="HuggingFaceTB/SmolLM2-1.7B-Instruct">SmolLM2-1.7B-Instruct (heavy)</option>
          <option value="__custom__">Custom model id…</option>
        </select>
      </label>

      <input id="customModel" class="hide" placeholder="e.g. Org/RepoName" size="28" />

      <button id="loadBtn" type="button">Load</button>
      <button id="clearBtn" type="button" class="secondary" disabled>Clear</button>

      <span class="status" id="status">Not loaded.</span>
    </div>

    <div class="chat" id="chat"></div>

    <div class="row">
      <input id="inp" placeholder="Type a message and press Enter…" disabled />
      <button id="sendBtn" type="button" disabled>Send</button>
    </div>

    <div class="small">
      If opening as <code>file://</code> blocks module imports on your machine, run a local server:
      <code>python -m http.server 8000</code> then open <code>http://localhost:8000</code>.
      First load downloads the model (can be large).
    </div>
  </div>

  <script type="module">
    const $ = (id) => document.getElementById(id);
    const chatEl = $("chat");
    const statusEl = $("status");
    const inp = $("inp");
    const sendBtn = $("sendBtn");
    const clearBtn = $("clearBtn");
    const loadBtn = $("loadBtn");
    const modelSelect = $("modelSelect");
    const customModel = $("customModel");

    function escapeHtml(s) {
      return String(s).replace(/[&<>"']/g, (c) => ({
        "&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"
      }[c]));
    }

    function addMsg(role, text) {
      const div = document.createElement("div");
      div.className = `msg ${role}`;
      div.innerHTML = `<span class="role">${role === "user" ? "You" : "AI"}:</span> ${escapeHtml(text)}`;
      chatEl.appendChild(div);
      chatEl.scrollTop = chatEl.scrollHeight;
    }

    function setUiLoaded(loaded) {
      inp.disabled = !loaded;
      sendBtn.disabled = !loaded;
      clearBtn.disabled = !loaded;
    }

    modelSelect.addEventListener("change", () => {
      const isCustom = modelSelect.value === "__custom__";
      customModel.classList.toggle("hide", !isCustom);
    });

    // Chat state
    let generator = null;
    let deviceUsed = "";
    const system = "System: You are a helpful assistant. Be concise.\n";
    let transcript = "";

    function resetChat() {
      transcript = "";
      chatEl.innerHTML = "";
      addMsg("ai", "Ready. Ask me a question.");
      inp.focus();
    }

    async function loadModel() {
      try {
        setUiLoaded(false);
        loadBtn.disabled = true;
        statusEl.textContent = "Loading library…";

        const { pipeline, env } = await import(
          "https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.0.2/+esm"
        );
        env.useBrowserCache = true;

        let modelId = modelSelect.value;
        if (modelId === "__custom__") modelId = customModel.value.trim();
        if (!modelId) throw new Error("No model id provided.");

        const make = async (device) => pipeline("text-generation", modelId, {
          dtype: "q4",
          device,
          progress_callback: (p) => {
            if (p && p.status === "progress") {
              const pct = (typeof p.progress === "number") ? ` ${p.progress.toFixed(1)}%` : "";
              statusEl.textContent = `Downloading ${p.file || ""}${pct}`.trim();
            }
          },
        });

        try {
          statusEl.textContent = "Initializing WebGPU…";
          generator = await make("webgpu");
          deviceUsed = "webgpu";
        } catch (e) {
          statusEl.textContent = "WebGPU failed, using WASM…";
          generator = await make("wasm");
          deviceUsed = "wasm";
        }

        statusEl.textContent = `Loaded ${modelId} (${deviceUsed}).`;
        setUiLoaded(true);
        resetChat();
      } catch (e) {
        console.error(e);
        statusEl.textContent = `Load failed: ${e.message || e}`;
        addMsg("ai", "Load failed. Check console. If using file:// and imports are blocked, run via a local server.");
        generator = null;
        deviceUsed = "";
        setUiLoaded(false);
      } finally {
        loadBtn.disabled = false;
      }
    }

    async function send() {
      if (!generator) return;

      const user = inp.value.trim();
      if (!user) return;

      inp.value = "";
      addMsg("user", user);

      transcript += `User: ${user}\nAssistant:`;
      statusEl.textContent = "Thinking…";
      sendBtn.disabled = true;
      inp.disabled = true;

      try {
        const out = await generator(system + transcript, {
          max_new_tokens: 160,
          temperature: 0.7,
          return_full_text: false
        });

        const r = Array.isArray(out) ? out[0] : out;
        const aiText = (r && r.generated_text != null) ? String(r.generated_text).trim() : "";
        transcript += ` ${aiText}\n`;

        addMsg("ai", aiText || "(no output)");
        statusEl.textContent = `Loaded (${deviceUsed}).`;
      } catch (e) {
        console.error(e);
        statusEl.textContent = "Generation error (see console).";
        addMsg("ai", "Error generating response. See console.");
      } finally {
        sendBtn.disabled = false;
        inp.disabled = false;
        inp.focus();
      }
    }

    sendBtn.addEventListener("click", send);
    inp.addEventListener("keydown", (e) => { if (e.key === "Enter") send(); });
    clearBtn.addEventListener("click", resetChat);
    loadBtn.addEventListener("click", loadModel);

    // Optional: auto-load on open
    // loadModel();
  </script>
</body>
</html>

The End of AI Monogamy: Let AI Find the Best Model for Your Task

TheRabbitHole — Fri, 16 Jan 2026 11:24:23 +0000

Most of us spend an insane amount of time using AI. Whether it's coding, writing, or analyzing data, we are glued to our prompts. But here is the problem: We are almost all "monogamous" with our AI. You probably have a subscription to ChatGPT, or maybe Claude, or Gemini. You know deep down that other models exist. You know that for certain tasks, a specialized model like DeepSeek or Llama 3 might be faster, cheaper, or smarter. But you don't switch.

Why?
Maybe it's not just the hassle of jumping into a new playground.
Or maybe It's that generic benchmarks rarely match reality.

We see leaderboards claiming a model is "#1 in Coding," but that is based on a standardized dataset. It doesn't tell you if the model is good at your specific legacy code, your unique tone of voice, or your particular data structure. A global average is meaningless when you have a specific problem.

This is inefficient. Relying on a general-purpose winner for every single task is a compromise. What if you didn't have to guess? What if your current AI assistant could run a "micro-benchmark" for you—using your actual prompt—right in the middle of your conversation?

The "Auto-Pilot" Benchmark

Example 1: Legacy Code Refactoring (Python)

You have a 500-line Django ORM query that's killing your database performance. Instead of asking ChatGPT and hoping:

"I have a 500-line Django ORM query that's killing our database performance. Run this code snippet through the top 3 LLM models on Replicate and show me their refactoring approaches side-by-side."

Why this works:

Claude might suggest async queries
DeepSeek might catch a specific database indexing issue
Llama might propose a completely different query structure
You see all three perspectives in parallel instead of re-prompting 3 times

Example 2: Data Analysis on Your Real Dataset

You have actual sales data and need insights:

"Here's my Q4 sales CSV. Find the top 3 models best at statistical reasoning, send them this data, and show me which model catches the most actionable insights."

Why this works:

GPT-4 might focus on trend analysis
Claude might catch subtle correlations you missed
Llama might be faster/cheaper and still identify key patterns
You're benchmarking on YOUR data, not generic datasets

Example 3: Multilingual Content with Brand Voice Matching

You need marketing copy in multiple languages with a specific tone:

"Write marketing copy for our premium SaaS in English, German, and Japanese. First, query which models are best at multilingual tone-matching, then run the same prompt through the top 2 models and show me the differences."

Why this works:

You see if one model nails your brand voice better
Some models are objectively better at specific languages
You pick the winner for each language instead of settling for one model

How It Works Under the Hood

By connecting an MCP (Model Context Protocol) client to live data sources, we bridge the gap between static leaderboards and active workflows.

Context Awareness: The AI detects if you are doing creative writing, logic puzzles, or hardcore engineering.
The Lookup: It queries the benchmark tool to find the highest-performing models for that specific category.
The Execution: It uses the Replicate API to spin up instances of those top models, feeds them your prompt, and aggregates the results.

You get 3 or 4 distinct answers from the smartest models on the planet, tailored exactly to the problem you are solving right now.

Disclaimer: The tools and workflows presented in this article provide a preliminary glimpse into the performance of various AI models, but these results should not be taken for granted. Automated comparisons are illustrative and may not reflect performance across all scenarios. To fully understand the specific strengths and weaknesses of candidate models, you must independently verify the results against your own data and requirements.

Getting Started

You only need two things:

Apify Account: Powers the benchmark scraping. Free account gives you $5/month in credits.
Replicate Account: Provides access to models. Pay-per-use, no monthly fees.

Step 1: Configure Your MCP Client

{
  "mcpServers": {
    "ai-live-benchmark": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://flamboyant-leaf--super-ai-bench-mcp.apify.actor/mcp",
        "--header",
        "Authorization: Bearer <APIFY_API_TOKEN>",
        "--header",
        "X-Replicate-API-Key: <REPLICATE_API_KEY>"
      ]
    }
  }
}

Step 2: Run the Test

Now for the fun part. We don't need to specify which benchmark to use. We just give the AI a task. Let’s try a specific multilingual request:

" Before you start, Read the Documentation. Then Find the 3 most powerful LLM models and run on Replicate to do this task: Write an email to my boss excusing being late in German."

Here is what happens next in real-time:

Phase A: The Smart Lookup

First, the AI analyzes your request. It realizes this is a text generation task involving a foreign language. It automatically decides to query the benchmark API for the current top-performing Large Language Models.

Phase B: Finding the Models

Next, it takes those top-ranked models and searches the Replicate "Model Garden" to see which ones are available for immediate access.

(Note: Sometimes a specific model version might not be hosted on Replicate. In that case, the agent is smart enough to just pick the next best model from the benchmark list—or you can simply ask it to "try the next one.")

Phase C: The Live Showdown

Finally, it runs the prediction. It doesn't just give you one answer; it executes the task on all three models in parallel.

Please note that sometimes Claude or another AI might guess the 'best' model by itself and start searching for it on Repclaiase. To avoid this, tell it explicitly to look up suitable benchmarks and let it search without outputting the result. This will give you a better understanding of what it is doing under the hood and what is suitable for your specific use cases.

Final thought

This isn't limited to emails or code. This workflow fully supports Image Models (Nano Banana, Qwen Image etc.) too. You can ask it to "Generate a cyberpunk city using the top 3 image models," and you will get a side-by-side comparison of Flux, Stable Diffusion, and others in one shot. And if you are using an interface like Claude Artifacts or Canvas, you can even ask the AI to build a simple HTML gallery to display these results side-by-side for a true "blind taste test." But that’s a topic for another post!

How to Check Mac Permissions: Audit Your Apps Using AI (5-Minute Guide)

TheRabbitHole — Mon, 12 Jan 2026 11:36:05 +0000

Apple's macOS privacy system is one of the most robust in the world, giving you granular control over which apps can access your camera, files, and microphone. But here's the problem: with hundreds of different settings—from camera access to voice and personal folders—it's nearly impossible to remember what you've actually granted.

The Hidden Problem: Permission Creep

Over months or years of use, you experience "permission creep" – unintentionally granting access to apps that don't really need it, or forgetting about settings you changed for a one-time task.

Examples include:

That Google Chrome camera permission from last year? Still there.
The Zoom microphone access you enabled for a single meeting? Probably still active.
Obscure apps with full disk access you never authorized?

Example: Multiple apps with microphone permissions enabled by default

Leaving these permissions open isn't just digital clutter. It's a genuine security risk.

Why Most Mac Users Are Vulnerable

Most Mac users never check their app permissions after granting them. This means:

Old apps retain access to your most sensitive data indefinitely
Untrustworthy applications can spy through your camera
Apps with full disk access can steal your financial documents
A single compromised app poses a real security threat

The good news? There's a simple way to check Mac permissions and use AI to automatically flag dangerous settings you missed. And you won't need any external tools—just your terminal and a smart AI like Claude or ChatGPT.

How to Check Mac Permissions: Step-by-Step

Step 1: Access Your Hidden Permission Database

Terminal is where you'll export your complete app permission database

macOS stores all your app permissions in a database called TCC.db. To check Mac permissions and export your complete permission database, open Terminal and run this single command:

sqlite3 ~/Library/Application\ Support/com.apple.TCC/TCC.db "SELECT * FROM access"

This outputs your entire permission database in raw format. It will look like cryptic gibberish at first, but that's exactly what AI is designed to organize and interpret.

Step 2: Feed Your Data to an AI Permission Auditor

Copy the entire output from your terminal. Paste it into Claude, ChatGPT, or your preferred AI tool. But don't stop there. You need to give the AI proper context so it understands what those database entries actually mean.

Provide a detailed prompt that explains the TCC.db structure. Ask the AI to:

Identify which permissions are unusual
Flag apps that shouldn't need camera or microphone access
Highlight security gaps and unexpected access patterns
Prioritize the most dangerous permission violations

Step 3: Review the AI Analysis

The AI will organize everything into readable tables and highlight suspicious entries. You'll immediately spot things like:

• Chrome with camera permissions you never intentionally granted
• Obscure apps with full disk access
• Old applications still retaining microphone permissions
• Unexpected access to your calendar, contacts, or photos

Full Disk Access and Screen Recording are among the most sensitive permissions to audit

Step 4: Revoke Unnecessary App Permissions in System Settings

Head to System Settings on your Mac. Navigate to Privacy & Security in the sidebar. Find each app the AI flagged and toggle off any permissions it shouldn't have using the official Apple interface. This ensures system stability and proper permission management.

What This Guide Actually Reveals: Complete Permission List

This audit gives you visibility into nearly every restricted resource on your Mac:

Media and Hardware:

Camera access
Microphone permissions
Bluetooth connectivity
Media Library access
Screen recording permissions

Personal Data Protection:

Photos and images
Reminders
Calendars
Contacts
Focus status
Full disk access (highest security priority)

Files and Folders:

Documents
Downloads
Desktop files
iCloud Drive
Network volumes

System Control:

AppleEvents (lets apps control other applications—often unnecessary)

Why This 5-Minute Permission Audit Matters

This five-minute audit shifts you from hoping you're secure to actually knowing which apps have access to your private life. It's the difference between trusting Apple's security system and actively managing it yourself.

Key takeaways:

Check Mac permissions at least quarterly
Remove permissions from apps you no longer use
Be suspicious of apps requesting camera or microphone access
Prioritize revoking full disk access when possible
Monitor your System Settings privacy dashboard regularly

A single compromised app with camera permission can spy on you. An app with full disk access can steal your financial documents. This audit prevents that from happening.

Final Thoughts

Don't leave your Mac's security to chance. Take five minutes today to check Mac permissions, and you'll gain peace of mind knowing exactly what each app can and can't access. Your digital privacy is too valuable to ignore.

Get the Complete AI Prompt Template

Visit the full article at airabbit.blog for the complete AI prompt template and step-by-step screenshots.

Image Credit
http://www.popsci.com/wp-content/uploads/2022/10/27/Change-App-Permissions-Mac.jpeg
http://images.squarespace-cdn.com/content/v1/6028101b47193120a4863356/9a417364-a4da-4bff-b491-4b0e6b20cabe/3+allow+apps+to+use+mic

http://cdn.document360.io/098100b7-b9da-4bea-b4b9-017140ab863e/Images/Documentation/Privacy-ScreenRecording.png

Runpod vs. Vast.ai: A Deep Dive into GPU Cloud Platforms for AI/ML

TheRabbitHole — Mon, 05 Jan 2026 16:42:22 +0000

The landscape of GPU cloud computing is rapidly evolving, with providers like Runpod and Vast.ai offering powerful, flexible, and often more cost-effective alternatives to traditional hyperscalers. For developers, researchers, and startups working with AI and machine learning, choosing the right platform can significantly impact project timelines, performance, and budget.

This post will compare Runpod and Vast.ai across key criteria to help you make an informed decision for your GPU-intensive workloads.

1. Core Value Proposition

Runpod: Positioned as "the most cost-effective platform for building, training, and scaling machine learning models" [1, 2]. Runpod emphasizes "more throughput, faster scaling, and higher efficiency," aiming to help users "get more done for every dollar" [2]. It offers a blend of persistent GPU instances (Pods) and auto-scaling serverless functions [3, 4].
Vast.ai: Highlights "More GPUs. More Control. Less Spend." [5]. Vast.ai functions as a global marketplace, providing access to "over 10,000 on-demand GPUs at prices 5–6x lower than traditional cloud providers" [5]. Its strength lies in real-time, competitive pricing driven by individual hosts [5, 6].

Verdict: Runpod offers a more managed and predictable experience, ideal for those who value stability and integrated solutions. Vast.ai appeals to users prioritizing the absolute lowest prices and a wider, albeit more variable, selection of hardware through its marketplace model.

2. GPU Offerings and Availability

Runpod: Boasts a wide range of NVIDIA GPUs, from high-end data center accelerators like H200 (141GB VRAM) [7], B200 (180GB VRAM) [7], H100 (SXM, PCIe, NVL with 80GB or 94GB VRAM) [8], A100 (SXM, PCIe with 80GB VRAM) [9], and AMD's MI300X (192GB VRAM) [10], to consumer-grade cards like RTX 5090 (32GB VRAM) [11], RTX 4090 (24GB VRAM) [12], RTX 3090 (24GB VRAM) [13], and professional cards like RTX 6000 Ada (48GB VRAM) [14], L40 (48GB VRAM) [15], and L4 (24GB VRAM) [16]. Availability is generally reliable, especially within their "Secure Cloud" managed data centers [3].
Vast.ai: Provides access to an extensive and diverse fleet of "10,000+ GPUs" through its decentralized marketplace [17]. This includes popular models like RTX 4090 (24GB VRAM) [6], H100 ("as little as $0.90/hour") [17], A100 [17], H200 [18], RTX 5090 [19], RTX 3090 [20], and RTX PRO 6000 (96GB VRAM) [21]. While the selection can be vast, availability and specific configurations (e.g., CPU, RAM, network) can fluctuate based on what individual hosts offer [6].

Verdict: For guaranteed access to specific, high-end, enterprise-grade GPUs with consistent configurations, Runpod is often more straightforward. Vast.ai is excellent for finding diverse hardware, often at aggressive price points, but requires flexibility due to its marketplace nature.

3. Pricing Models & Cost Efficiency

Runpod:
- Pods (Persistent Instances): Offers On-Demand (e.g., H100 NVL at $3.07/hr [10], RTX 4090 at $0.59/hr [12]), Savings Plans (3, 6, 12-month commitments for discounts, e.g., H100 PCIe at $2.25/hr on a 3-month plan, compared to $2.39/hr On-Demand [22]), and Spot instances (lowest cost, interruptible, e.g., H100 SXM at $1.75/hr [23]). Spot instances are described as "Access spare compute capacity at the lowest prices. These instances are interruptible" [24].
- Serverless: Billed per second for both "Flex" (auto-scaling, cost-efficient for bursty workloads) and "Active" (always-on, no cold starts, up to 30% discount) [25, 26]. Examples for Active workers per second: H200 PRO $0.00155/s [25], H100 PRO $0.00116/s [25], RTX 4090 PRO $0.00031/s [25].
- Storage: Clear pricing for Container Disk ($0.10/GB/month) [27], Disk Volumes ($0.10/GB/month on running Pods, $0.20/GB/month for stopped Pods) [27], and Network Volumes ($0.07/GB/month under 1TB, $0.05/GB/month over 1TB) [27, 28]. Critically, Runpod explicitly states zero ingress/egress fees [2, 7].
Vast.ai:
- Instances (GPU Cloud): Provides On-Demand, Reserved (up to 50% discount with commitment), and Interruptible (spot) instances, with interruptible instances "often 50%+ cheaper than on-demand" [29]. Pricing is hourly and marketplace-driven, meaning it can vary significantly. Example RTX 4090 prices seen range from $0.338/hr (for a 4x RTX 4090 setup) to $0.540/hr (for a 1x RTX 4090) [6].
- Serverless: Pay-as-you-go, per-second billing at the same rates as non-Serverless GPU instances [30].
- Storage & Bandwidth: Instances accrue storage costs per second, even when stopped [31]. "Data transfer costs vary by host and include both upload and download traffic. Charges apply per byte transferred" [29, 32].

Verdict: Vast.ai often wins on raw hourly GPU compute cost, especially for Interruptible instances, making it attractive for budget-conscious, fault-tolerant workloads. However, Runpod's transparent storage and absence of ingress/egress fees can lead to significant cost savings, especially for large datasets or frequent data movement. Runpod's Serverless pricing model, with its granular per-second billing and options for managing cold starts, is highly competitive for inference.

4. Workload Types & Key Features

Runpod:
- Pods: "Create and manage persistent GPU instances for development, training, and long-running workloads" with programmatic SSH access [33].
- Serverless: "Deploy and scale containerized applications for AI inference and batch processing" with automatic scaling from zero to hundreds of workers [33, 4]. Features like "FlashBoot" for "<200ms cold-starts" and "Zero cold-starts with active workers" are available [25]. It offers pre-built templates for popular tools like Axolotl (fine-tuning), ComfyUI (image generation), and vLLM (fast LLM inference) [34].
- Instant Clusters: Offers "fully managed compute clusters for multi-node training and AI inference" with "high-speed networking from 1600 to 3200 Gbps" [35]. These clusters support H200, B200, H100, and A100 GPUs and are orchestrated with Slurm [35, 36].
- Runpod Hub: Described as "The fastest way to deploy open-source AI," providing "one-click deployment" with prebuilt Docker images and Serverless handlers [37].
Vast.ai:
- GPU Cloud (Instances): Provides flexible GPU compute for a wide range of tasks with "On-Demand GPU Deployment" [17].
- Serverless: Features "Dynamic Scaling" for AI inference [38]. A notable security feature is that "client send payloads directly to the GPU instances, your payload information is never stored on Vast servers" [39].
- Clusters: Offers "High-Performance AI & HPC Clusters" for large-scale training and inference, compatible with ML frameworks (TensorFlow, PyTorch) and container-based workflows (Docker, Kubernetes) [40].
- Hosting: Uniquely allows individuals to rent out their own GPUs [21], contributing to the diverse marketplace.

Verdict: Both platforms cater to training and inference workloads effectively. Runpod offers more structured, enterprise-ready solutions with Instant Clusters and its curated Hub for streamlined model deployment. Vast.ai's strength lies in its raw compute power accessible via its marketplace and the unique hosting model. Vast.ai's Serverless security model, where payloads aren't stored on Vast servers, is a notable advantage for certain use cases.

5. Ease of Use & Developer Experience

Runpod: Provides a user-friendly console (implied by the design of deployment pages like [10]), a comprehensive API [33], and a CLI (mentioned in documentation sidebars, e.g., [41] for programmatic management. Its offerings are clearly delineated, with many pre-configured templates and Docker images to simplify setup [34].
Vast.ai: Offers a web console, API, and CLI (mentioned as "fully automated via API & CLI" [17]). The marketplace interface, while powerful, can sometimes be overwhelming due to the sheer volume and variability of listings [6]. Templates are available to ease deployment (e.g., various templates linked from cloud.vast.ai).

Verdict: Runpod generally offers a more streamlined and intuitive experience, particularly for those looking for direct deployment without extensive searching or configuration. Vast.ai requires a bit more effort to navigate its marketplace but rewards users with incredible flexibility and potential cost savings.

6. Security & Reliability

Runpod: Is "officially SOC 2 Type II Compliant" [1, 2], indicating a strong commitment to security controls. It offers a "Secure Cloud" tier that "operates in T3/T4 data centers, providing high reliability and security for enterprise and production workloads," alongside a "Community Cloud" for more budget-friendly options [3].
Vast.ai: States "SOC 2 Type I compliance" [17, 42] and emphasizes "Secure Cloud Isolation" [17]. As a marketplace, the reliability can depend on individual hosts, though Vast.ai provides host "Reliability" scores (e.g., 99.85%) to guide user choice [29, 6].

Verdict: Runpod's SOC 2 Type II certification represents a higher standard of security auditing. Its explicit distinction between Secure and Community Clouds gives users clear expectations regarding reliability and guarantees. Vast.ai's marketplace nature inherently introduces variability in host reliability, though mechanisms are in place to mitigate this.

Conclusion: Which Platform is Right for You?

The choice between Runpod and Vast.ai depends heavily on your specific needs:

Choose Runpod if:
- You prioritize predictable pricing and guaranteed resource availability [24].
- You need enterprise-grade security and reliability (SOC 2 Type II, Secure Cloud) [1, 3].
- You require structured multi-node training with high-speed interconnects (Instant Clusters) [35].
- You want a streamlined experience for deploying open-source AI models (Runpod Hub) or auto-scaling inference (Serverless with FlashBoot/Active Workers) [37, 25].
- You want to avoid hidden costs like egress fees [2].
Choose Vast.ai if:
- Your primary concern is finding the absolute lowest GPU prices on the market [5].
- You have fault-tolerant workloads that can leverage interruptible instances [29].
- You need access to a very diverse range of GPU hardware and are comfortable with marketplace dynamics [17].
- You are a host looking to monetize your own GPUs [21].
- You appreciate the direct payload routing for serverless inference from a security perspective [39].

Both platforms are innovating to make GPU computing more accessible and affordable. By carefully evaluating your project requirements, budget, and tolerance for variability, you can select the platform that best accelerates your AI/ML journey.

References:

Vast.ai:
[5] https://vast.ai/pricing
[6] https://cloud.vast.ai/?gpu_option=RTX%204090
[17] https://vast.ai/products/gpu-cloud
[18] https://cloud.vast.ai/?gpu_option=H200
[19] https://cloud.vast.ai/?gpu_option=RTX%205090
[20] https://cloud.vast.ai/?gpu_option=RTX%203090
[21] https://cloud.vast.ai/create (also lists RTX PRO 6000)
[29] https://docs.vast.ai/documentation/instances/pricing
[30] https://docs.vast.ai/documentation/serverless/pricing
[31] https://docs.vast.ai/documentation/reference/billing-help
[32] https://docs.vast.ai/documentation/reference/billing
[38] https://docs.vast.ai/documentation/serverless
[39] https://docs.vast.ai/documentation/serverless/architecture
[40] https://vast.ai/products/clusters
[21] https://cloud.vast.ai/host/setup
[42] https://vast.ai/products/serverless

3 Best Ways to Copy Text When Right-Click is Disabled (2026 Guide)

TheRabbitHole — Sat, 03 Jan 2026 16:41:17 +0000

We often need to extract and analyze web-based text from sources where standard copy-pasting is disabled. This can be due to platform design (like chat applications), proprietary content protections, or intentional copy-blocking measures on articles and documents.
This guide presents a simple two-step method to overcome these technical limitations without requiring extensions, complicated workarounds, or legal concerns. By capturing a full-page image and using an AI assistant, you can effectively extract and interact with any web content, turning restricted information into a valuable resource for analysis and documentation.

Disclaimer: This guide is for lawful purposes only. Users are solely responsible for ensuring their use complies with all applicable laws, terms of service, and copyright regulations.

You must own or have permission to access the content you capture

Respect copyright laws and intellectual property rights

Verify compliance with website terms of service

Do not use extracted content for unauthorized commercial purposes

Check local laws regarding screen captures and data extraction The authors assume no liability for misuse of this method.

The Challenge: Why Websites Block Copy Functions

You may encounter scenarios where you need to extract and analyze text from a web source, but the standard copy function is unavailable. Common situations include:

Chat Conversations: Reviewing lengthy discussions with business partners to summarize key decisions
Proprietary Content: Analyzing competitor websites or paywalled articles
Chat Applications: Exporting conversations from Slack, Discord, or WhatsApp Web
Protected Documents: Accessing text from PDFs or images that prevent copy-paste
Forum Discussions: Saving important technical information from read-only forums
Email Archives: Extracting text from archived emails that disable selection

This creates a significant barrier to efficient data handling, research, and knowledge management.

The Solution: A Two-Step Capture and Analysis Technique

Our method bypasses copy-protection by working around the technical limitation. Instead of trying to extract text directly from the protected source, we:

Capture the visual content as an image
Use AI to read and extract the text from that image

Step-by-Step Implementation

Step 1: Capture the Entire Webpage with GoFullPage (1-2 minutes)

The first step is to create a comprehensive, full-page image of all the content you wish to analyze.

Image source: Softpedia

Why GoFullPage?

Open-source and free
Works on Chrome, Edge, and Brave
Captures long, scrollable pages in one image
No login required or data collection

Installation and Use:

Image source: Aiseesoft

Install the Extension:
- Open Chrome Web Store
- Search for "GoFullPage" by Jothan
- Click "Add to Chrome" and confirm
Navigate to Your Target:
- Open the webpage or chat window you want to capture
- Make sure the content is fully loaded on screen
Capture the Page:
- Click the GoFullPage extension icon in your browser toolbar
- The extension automatically scrolls through the entire page and captures it
- A new tab opens showing your full-page screenshot
- Wait for the image to fully generate (10-30 seconds)

Image source: Microsoft Store

Copy the Image to Clipboard:
- Right-click on the full-page image
- Select "Copy Image"
- The image is now ready for AI analysis

Step 2: Extract Text Using AI

With the image on your clipboard, use a multimodal AI assistant to extract and analyze the text.

Option A: Google Gemini

Open Google Gemini:
- Go to gemini.google.com
- Sign in with your Google account (free)
Paste Your Screenshot:
- In the chat input field, press Ctrl+V (Windows) or Cmd+V (Mac)
- The image will appear in the chat
Ask for Text Extraction:
- Type: "Extract all the text from this screenshot and provide it as plain text"
- Gemini will process the image and respond with extracted text
Copy the Results:
- Select and copy the extracted text
- Use for documentation, analysis, or further processing

Option B: OpenAI ChatGPT

Go to chat.openai.com
Start a new chat
Click the attachment icon and upload your screenshot
Ask: "Extract all text from this image and provide it as plain text"
ChatGPT will extract the text

Image source: Easy With AI

Option C: Claude AI (Anthropic)

Free option with excellent image processing:

Go to claude.ai
Create free account or sign in
Click the attachment icon to upload your screenshot
Request: "Please extract all visible text from this screenshot"
Claude provides clean, formatted text extraction

Common Use Cases

1. Extracting Text from Chat Conversations

Capture a Slack or Discord conversation
Paste into AI → Request summary
Get organized transcript with key points

2. Saving from Personal Email Archives

Screenshot your own email threads
Extract for documentation purposes
Create searchable email backup

3. Collecting Your Own Notes

Screenshot notes from personal projects
Extract for compilation into documents
Archive for compliance or reference

4. Accessing Your Own Content

Screenshots of your own social media posts
Extract for content repurposing
Backup your own digital content

Alternative Methods

Method 2: Print to PDF + AI Extraction

Press Ctrl+P (Windows) or Cmd+P (Mac)
Select "Save as PDF"
Upload PDF to AI tool (Gemini, ChatGPT, or Claude)
Request text extraction

Method 3: Built-in Windows 11 OCR

For Windows 11 users:

Take screenshot with Snipping Tool
Click the text icon in Snipping Tool
Text is automatically extracted
Copy directly to clipboard

Image source: All Things How

Image source: MundoBytes

Method 4: Browser Developer Tools

Press F12 to open Developer Tools
Go to "Elements" tab
Find the text in HTML
Copy from source code

Image source: Microsoft Edge Documentation

Limitations & Important Considerations

What This Method CAN'T Do:

Extract from videos or audio
Maintain perfect formatting on complex layouts
Process extremely small text (under 8pt font)
Extract image files as images

Accuracy:

Handwritten text: 60-70% accuracy
Low-resolution images: May have errors
Multiple languages: Works but verify results
Special characters: Usually extracted correctly

Real-World Example Workflow

Scenario: You need to extract your own Slack conversation for personal documentation

Step 1: Prepare (30 seconds)

Open your Slack conversation
Scroll to the first message you want
Install GoFullPage (one-time)

Step 2: Capture (1 minute)

Click GoFullPage icon
Wait for image generation
Right-click → Copy Image

Step 3: Extract (1 minute)

Open gemini.google.com
Paste screenshot
Type: "Extract all messages"
Copy the results

Step 4: Use (varies)

Paste into Word or Notion
Archive for personal records
Create backup documentation

Total time: 3-4 minutes

Pro Tips for Better Results

For Long Pages:
- Zoom to 80-90% before capturing
- Results in clearer OCR extraction
For Low Contrast:
- Switch to light mode before capturing
- Improves extraction accuracy
For Multiple Screenshots:
- Number your captures
- Tell AI to combine them in order
For Verification:
- Compare extracted text with original
- Takes 2-3 minutes for quality check

Conclusion

By following this two-step workflow, you can overcome technical restrictions and interact with your own content in meaningful ways. Whether you're a professional documenting conversations, a student saving your own notes, or a content creator backing up your work—this method provides a reliable, free solution.

The Automated Time Tracking Showdown: Which Tool Actually Works in 2025?

TheRabbitHole — Thu, 01 Jan 2026 13:47:51 +0000

If you've ever wondered where your day actually goes, you're not alone. Most professionals waste hours trying to manually log their time, remember which project they worked on three hours ago, or explain productivity gaps to their managers. This is where automated time tracking solutions come in, and the market has exploded with options.

The problem is that not all time tracking tools are created equal. Some are obsessed with surveillance-style monitoring that makes employees feel watched. Others offer such minimal features that they become useless after a month. And then there are the ones that promise AI-powered insights but deliver nothing but marketing hype.

In this comprehensive guide, we'll compare RescueTime, Toggl Track, Clockify, Timing (Mac), DeskTime, and AutoJournal AI. We’ll look beyond marketing claims and dig into what actually matters: privacy, automatic tracking quality, actionable insights/AI, platform support, pricing/true cost, integrations, and real-world fit.

1) Privacy: Who’s Really Watching Your Screen?

Privacy is the elephant in the room when it comes to time tracking. We’ve all heard the stories about companies tracking mouse movements, monitoring keystrokes, and taking random screenshots. The question isn’t just “does it track my time?” but “does it respect my privacy while doing it?”

RescueTime

RescueTime tracks every application you open, every website you visit, and every file you work on. While RescueTime doesn't take screenshots by default (unless you enable them), it does maintain detailed logs of your digital activity that get synced to their servers. This data is anonymized and used to power their analytics, but if you're privacy-conscious, knowing that detailed records of your browsing and app usage are stored somewhere makes many people uncomfortable.

Dashboard displaying productivity metrics, Pulse Score, activity categories, and productivity trends with color-coded application usage (green=productive, blue=neutral, red=distracting)

Toggl Track

Toggl Track takes a similar approach—it's application and website aware, tracking your activity across tools.

Reports dashboard showing billable/non-billable hours breakdown, projects, team members, and hourly allocation metrics designed for client billing

Clockify

Clockify positions itself as the "transparent" option within the surveillance camp. It shows you exactly what it's tracking and lets you block certain applications and websites from being monitored. However, the data still gets sent to Cloudflare's servers, and there's inherently less privacy than a local-processing approach.

Daily hours worked, pie charts breaking down time by project/task, team activity overview, and straightforward time allocation visualization

Timing (Mac)

Timing for Mac is unusual because it's actually a native Mac application that does most of its processing locally. It tracks application and website usage in detail, but the analysis happens on your machine. However, this advantage is only available to Mac users, limiting its appeal for cross-platform teams.

Timeline interface showing minute-by-minute activity organized by application and document, plus 30-day activity graph with weekly work patterns

DeskTime

DeskTime is the most transparent about its surveillance nature. It's designed explicitly for team monitoring, with managers able to view detailed activity logs, screenshots, and productivity scores for their employees. If you're running a remote team and want oversight, this is the most honest tool available. But if you're an individual or a company that values privacy, DeskTime should be a hard pass.

Employee metrics dashboard showing individual productivity scores, activity categories breakdown, and detailed work tracking (manager perspective)

AutoJournal AI

AutoJournal AI is described as a lightweight Mac tracker that builds a detailed timeline of your day solely using window activity. It does not take screenshots and does not track keystrokes, and it does not upload your journal to the cloud.

Logs only the active Window Title and process name
Data stored locally in an efficient database on your hard drive
Zero internet connection required for core tracking capabilities
“100% Offline Privacy”: journal data never leaves your machine

Privacy winner (based on the claims in the text you provided): AutoJournal AI (offline, no screenshots, no keystrokes, no cloud uploads).

Automatic Tracking and Detection: How Well Does Each Tool Understand Your Work?

The real test isn’t the dashboard—it’s whether the tool can capture reality without requiring constant manual entry.

RescueTime

RescueTime watches which applications are in focus and maintains a database of websites to categorize them. It works reasonably well for obvious activities, but it struggles with context. If you're using a web browser to work on three different projects across three different tabs, RescueTime can only see “Web Browser” and guesses based on the domain.

Toggl Track

Toggl Track is primarily designed for manual time entry, but it offers browser extensions that can automatically create time entries based on your browsing activity. This hybrid approach appeals to teams that want flexibility, but it’s not described as having calendar/email context.

Clockify

Clockify is primarily manual time tracking with browser and desktop convenience. The application can detect when you're actively using the tool versus idle, but it doesn't have deep contextual understanding of what you're working on.

Timing (Mac)

Timing for Mac stands out as having one of the most granular tracking systems available. It monitors applications, websites, document titles, and more. The catch: you can end up with more data than you know what to do with—turning raw activity into meaningful “project work” becomes the challenge.

DeskTime

DeskTime offers extremely detailed activity tracking: application usage, website visits, and it can take screenshots. The goal is maximum visibility for managers, but the downside is that it can feel invasive.

AutoJournal AI

AutoJournal AI tracks window metadata (active Window Title + process name) and aims for detailed granularity without “heavy visuals.” It’s described as:

fast & light (minimal CPU/RAM) by tracking window metadata instead of heavy visuals
detailed enough to know which file you were editing, which website you visited, and for how long
able to track apps, specific windows, and peak hours

Tracking winner (as framed in your original draft): Timing (Mac) for maximum granularity.
AutoJournal AI is positioned as detailed journaling via window metadata while staying offline and lightweight.

AI Features and Actionable Insights: Where The Intelligence Actually Lives

Here’s the dirty secret about most time tracking tools: they’re databases with dashboards. They can tell you what happened, but not always what it means.

RescueTime

RescueTime offers “insights,” but they’re described as fairly basic: productivity scores based on “productive vs unproductive” apps and focus/distraction patterns. The intelligence stops there.

Toggl Track

Toggl’s intelligence is in reporting: slice time entries by project, client, task, and team member. It’s a tool for tracking time, not for interpreting it.

Clockify

Clockify is straightforward reporting and aggregation. If you want “intelligence,” you interpret the numbers yourself.

Timing (Mac)

Timing offers some pattern recognition: most productive hours, app-switching frequency, focus sessions. It’s more “here’s what happened” than “here’s what this means for your priorities.”

DeskTime

DeskTime’s AI features focus on employee productivity scoring using proprietary algorithms based on computer usage. The methodology isn’t fully transparent, and the draft notes debate around whether computer activity correlates with output.

AutoJournal AI

AutoJournal AI includes a built-in feature: chat with your Work Journal using built-in AI inside the app. Examples you provided:

“How many hours did I work today?”
“What were my top 5 tasks by time spent?”
“Summarize my development work vs meetings.”

It also includes an advanced option: connect via MCP (Model Context Protocol) to external AI tools like ChatGPT, or any MCP-compatible AI assistant. MCP is optional, and the journal is only shared when you explicitly connect.

AI winner (based on the features you provided): AutoJournal AI (built-in AI chat + optional MCP connection to external AI).

Platform Support: Windows, Mac, Linux, and Mobile

Not every tool needs every platform, but mismatches here are instant dealbreakers.

RescueTime: strongest historically on Mac; Windows exists but less polished; Android/iOS exist but limited vs desktop
Toggl Track: web app + Chrome extension + native Windows/Mac apps + solid iOS/Android apps
Clockify: web everywhere + native Windows/Mac + mobile support
Timing (Mac): macOS only
DeskTime: Windows and Mac apps; mobile support for monitoring
AutoJournal AI: Download for Mac (macOS)

Platform winner (from your draft): Toggl Track for broad cross-platform support.

Pricing and True Cost of Ownership: What Are You Actually Paying For?

Pricing differences only matter after you consider real usage and overhead.

From your draft:

RescueTime: free tier; $14.99/month per individual; no explicit team pricing (teams buy individual licenses)
Toggl Track: free plan up to 50 users with 1 project; paid starts at $99/month for up to 5 users (Starter); enterprise $300–500+/month
Clockify: unlimited free plan; paid starts at $7/user/month or $99/month for unlimited team members
Timing (Mac): $9.99/month or $99.99/year (single user; no team version)
DeskTime: individual plans from $29/month; teams typically $35–40 per employee per month
AutoJournal AI: the text you provided states free trial available, no credit card required (it does not provide monthly pricing numbers)

Your draft’s “5-person consulting firm billing time” example (as written):

AutoJournal AI: $35–60/month
RescueTime: $75/month (5 × $14.99)
Toggl Track: $99/month (Starter)
Clockify: $35/month (5 × $7) or free if you don’t need team features
Timing: N/A (Mac only, no team plan)
DeskTime: $175–200/month (5 × $35–40)

Pricing winner (as your draft framed it):

Clockify for basic tracking on a budget
Toggl Track if you need billing/reporting
AutoJournal AI if automatic tracking + insights reduce manual effort (noting: the product copy you provided only explicitly states free trial/no card)

Integration Ecosystem: Does It Play Well With Your Other Tools?

No productivity tool exists in isolation.

From your draft:

RescueTime: integrations with Slack, Google Sheets, and an API
Toggl Track: Slack, JIRA, Asana, Monday, Google Sheets, and 50+ via Zapier
Clockify: API, Zapier/webhooks, direct integrations with Jira/Asana
Timing (Mac): basic API; smaller ecosystem
DeskTime: limited integrations; focused on monitoring
AutoJournal AI: MCP connection to external AI tools (ChatGPT + any MCP-compatible AI); optional and explicit opt-in sharing

Integration winner (from your draft): Toggl Track for breadth.

Real-World Use Cases: Who Should Actually Use Each Tool?

Scenario 1: Freelance Designer Working Solo

Clockify if you want the cheapest basic tracker and don’t mind manual entry.
AutoJournal AI if you’re on Mac and want an offline, detailed work journal built from window activity.

Scenario 2: Engineering Team (5–10 people) at a Startup

Toggl Track for cross-platform tracking and reporting.
DeskTime only if the goal is management oversight and monitoring.

Scenario 3: Remote Work Agency (15–20) Serving Corporate Clients

Toggl Track for billable/non-billable breakdowns and client-oriented reporting.

Scenario 4: Corporate Compliance and Employee Oversight

DeskTime for explicit monitoring (screenshots, manager dashboards).
RescueTime as the less screenshot-centric approach (screenshots not default).

Scenario 5: Consultant Seeking Personal Insight (Mac)

Timing (Mac) if you want maximum granularity and don’t mind a lot of raw data.
AutoJournal AI if you want an offline window-based journal plus built-in AI Q&A.

Putting it all together

Category	RescueTime	Toggl Track	Clockify	Timing (Mac)	DeskTime	AutoJournal AI
Privacy stance	Detailed activity logs synced to servers; screenshots optional if enabled	App + website aware activity tracking	Shows what it tracks; block apps/sites; data sent to Cloudflare servers	Local processing on Mac	Surveillance-style team monitoring; screenshots + productivity scores	100% offline; data never leaves machine; no screenshots; no keystrokes; no cloud uploads
What it tracks	Apps, websites, files	Apps + websites; manual time entry workflows	Manual-first; idle/active; blocking apps/sites	Apps, websites, document titles, more	Apps, websites; optional screenshots	Active Window Title + process name (window metadata)
AI / insights	Productivity scores + focus/distraction patterns	Reporting by project/client/task/team	Reporting/aggregation	Pattern recognition (productive hours, app-switching, focus sessions)	Employee productivity scoring (proprietary)	Built-in AI chat; optional MCP to ChatGPT/any MCP AI (explicit opt-in)
Platform	Mac strong; Windows exists; iOS/Android limited vs desktop	Web + Windows/Mac + iOS/Android + extension	Web + Windows/Mac + mobile	macOS only	Windows/Mac + mobile monitoring	macOS
Integrations (stated)	Slack, Google Sheets, API	Slack, Jira, Asana, Monday, Sheets, Zapier	API, Zapier/webhooks, Jira/Asana	Basic API	Limited	MCP external AI connection (optional)
Pricing (stated)	Free tier; $14.99/mo individual	Free: up to 50 users, 1 project; paid from $99/mo (5 users)	Unlimited free; paid $7/user/mo or $99/mo unlimited team	$9.99/mo or $99.99/yr	$29/mo individual; ~$35–40/employee/mo team	Free trial; no credit card required (pricing numbers not in the product copy you supplied)

9) The Privacy and Ethics Section: Why This Matters More Than You Think

Your draft notes: research from Microsoft and Stanford shows employee surveillance correlates with lower productivity and engagement, not higher. When people feel watched, stress increases and cognitive performance drops—so monitoring can backfire.

It also notes growing legal/regulatory pressure (GDPR provisions and “right to disconnect” laws) and culture/talent impacts: privacy-invasive monitoring is increasingly seen as a red flag.

Which Tool Should You Actually Choose?

RescueTime: best fit if you want detailed personal productivity tracking and accept server-synced activity logs.
Toggl Track: best fit for client billing, project reporting, integrations, and cross-platform teams.
Clockify: best fit for budget tracking with straightforward reporting (and an unlimited free plan).
Timing (Mac): best fit for Mac-only users who want maximum granular tracking and are willing to manage lots of data.
DeskTime: best fit for organizations that want explicit employee monitoring and oversight.
AutoJournal AI: best fit for Mac users who want an offline, private, window-metadata-based work journal (no screenshots, no keystrokes, no cloud uploads) plus built-in AI Q&A and optional MCP connection to external AI tools.