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Claude AI Cracks 11-Year-Old BTC Wallet

Hussam Laurent — Thu, 14 May 2026 18:25:48 +0000

A Bitcoin trader recovered a long-lost wallet containing $400,000 worth of BTC this week using Anthropic's Claude AI, a story that first surfaced on Hacker News and quickly amassed 261 points and 132 comments.

AI: Claude | Task: Password cracking | Attempts: 3.5 trillion | Outcome: Recovered $400,000 BTC wallet

How Claude AI Cracked the Password

Claude AI, developed by Anthropic, processed an encrypted wallet backup by systematically testing password combinations. The process involved generating and verifying guesses based on patterns in the user's historical data, ultimately succeeding after 11 years of the wallet being inaccessible. This demonstrates Claude's capability for brute-force tasks enhanced by its large language model architecture, which analyzes context to prioritize likely passwords.

Key Numbers from the Recovery

The recovery required Claude to attempt 3.5 trillion passwords, taking an unspecified amount of time but highlighting the AI's efficiency in handling massive computations. HN comments noted the wallet held 13.7 BTC at the time of recovery, valued at $400,000 based on current prices. Compared to traditional methods, this event shows AI reducing what could take humans years into a feasible operation, with Claude's processing speed outpacing manual efforts by orders of magnitude.

Bottom line: Claude's ability to handle 3.5 trillion attempts underscores its potential for accelerating cryptographic tasks, far exceeding human limits.

Trying Similar AI Tools

To replicate this for personal use, start with Anthropic's Claude interface via their website or API. Users can upload encrypted files and prompt the AI with commands like "generate password guesses for this file," but always ensure ethical compliance. For developers, access Claude through the Anthropic API documentation, where integration requires a paid account starting at $5 per million tokens. Community tools on GitHub, such as password-cracking scripts adapted for LLMs, provide starting points, but test on non-sensitive data first.

"Step-by-Step Setup"

Install Python and the Anthropic SDK: pip install anthropic
Obtain an API key from console.anthropic.com
Run a basic prompt: claude.messages.create(model="claude-3-5-sonnet", messages=[{"role": "user", "content": "Crack this password pattern: ..."}])
Limit to small-scale tests to avoid legal issues

Advantages and Drawbacks of Using AI for Security

AI like Claude offers speed advantages, processing trillions of combinations faster than human-operated tools. However, it risks exposing vulnerabilities if used improperly, as seen in this case where the wallet's age made it susceptible. Drawbacks include high computational costs, potentially hundreds of dollars in API fees for extensive runs, and ethical concerns around unauthorized access.

Pros: Accelerates recovery for forgotten credentials; leverages pattern recognition for efficiency
Cons: Raises security risks if misused; depends on API availability, which can change with Anthropic's updates

Alternatives and Comparisons

Other AI models for similar tasks include OpenAI's GPT-4, which handles pattern-based predictions, and specialized tools like John the Ripper for brute-force attacks. Below is a comparison based on speed, cost, and capabilities:

Feature	Claude	GPT-4	John the Ripper
Speed (attempts)	3.5 trillion	Up to 1 trillion (per session)	Variable, hardware-dependent
Cost	$5+ per million tokens	$0.01 per 1,000 tokens via API	Free (open-source)
Ease of Use	API integration	Chat interface	Command-line scripts
Security Focus	General AI	Versatile prompts	Dedicated cracking

Claude edges out in integrated AI features, but GPT-4 offers broader customization through OpenAI's playground, while John the Ripper remains faster on local hardware for simple patterns.

Bottom line: Claude excels in AI-driven efficiency for complex guesses, but free alternatives like John the Ripper suit budget users without needing cloud resources.

Who Should Use This

This approach benefits security researchers testing encryption strength or individuals with forgotten passwords on old backups. Developers building recovery tools might adopt Claude for its AI insights, given its success in this high-stakes scenario. Avoid it if you're in regulated industries like finance, where automated cracking could violate laws, or if you lack the expertise to handle potential data breaches.

Final Verdict

In summary, Claude's role in this recovery highlights AI's growing utility in real-world security challenges, potentially saving users significant losses. As AI models continue to evolve, expect more applications in cryptography, though users must weigh the ethical and legal implications carefully. This event positions Claude as a leader in practical AI solutions, paving the way for safer digital asset management.

Python Rootkit Threatens Linux Kernels Since 2017

Hussam Laurent — Fri, 01 May 2026 00:25:39 +0000

Black Forest Labs released FLUX.2 [klein], a compact model series for real-time local image generation and editing. This advancement targets AI creators needing efficient tools on consumer hardware, generating 1024x1024 images in under one second.

This article was inspired by "FLUX.2 klein launch" from Hacker News.

Read the original source.

Model: FLUX.2 [klein] | Parameters: 4B / 9B | Speed: 0.3-0.5s per image

VRAM: 8.4 GB (4B) / 19.6 GB (9B) | License: Apache 2.0 (4B) / Non-commercial (9B)

What It Is and How It Works

FLUX.2 [klein] is a text-to-image model series from Black Forest Labs that combines generation and editing capabilities in a single architecture. The 4B parameter variant processes prompts to create images quickly, while the 9B version enhances photorealism. Both models use a unified framework, allowing users to generate an image from text and then edit it directly, reducing the need for separate tools.

Benchmarks and Specs

The 4B model achieves 0.3 seconds per 1024x1024 image, making it 30% faster than competitors like Stable Diffusion on similar hardware. It requires only 8.4 GB of VRAM on an RTX 4070, enabling real-time performance without optimizations. The 9B model, at 0.5 seconds per image, demands 19.6 GB of VRAM for better quality outputs.

Feature	FLUX.2 klein 4B	FLUX.2 klein 9B	Stable Diffusion XL
Speed	0.3s	0.5s	0.4-0.6s
VRAM	8.4 GB	19.6 GB	12-16 GB
Parameters	4B	9B	7B
Editing	Yes	Yes	Limited

How to Try It

Users can access FLUX.2 [klein] via Hugging Face for local setup. Download the model with huggingface-cli download black-forest-labs/FLUX.2-klein --local-files-only. For the 4B variant, run it in a Python environment using PyTorch: import and generate images with a simple prompt like "a cat in a hat". API access is available through Black Forest Labs' platform, with pricing starting at $0.01 per image.

"Full Setup Steps"

Install dependencies: pip install torch diffusers
Load the model: from diffusers import FluxPipeline; pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.2-klein-4B')
Generate: image = pipeline("prompt here").images[0]
Community nodes for ComfyUI are on GitHub, enabling custom workflows.

Pros and Cons

The 4B model's low VRAM requirement makes it accessible for laptops, ideal for on-the-go AI creators. Its unified editing feature saves time by avoiding tool switches, with Apache 2.0 licensing allowing commercial use. However, the 9B model's non-commercial license limits business applications, and both may produce less detailed outputs compared to larger models.

Pros: Fast generation on consumer GPUs; integrated editing; open licensing for smaller variant
Cons: Potential quality trade-offs in 4B; higher resource needs for 9B; limited fine-tuning options

Alternatives and Comparisons

FLUX.2 [klein] competes with Stable Diffusion XL and Qwen-Image-Edit, both of which handle text-to-image tasks but lag in speed. Stable Diffusion XL requires more VRAM for similar speeds, while Qwen-Image-Edit excels in editing but takes 2 seconds per image.

Feature	FLUX.2 klein 4B	Stable Diffusion XL	Qwen-Image-Edit
Speed	0.3s	0.4s	2s
VRAM	8.4 GB	12 GB	20+ GB
License	Apache 2.0	CreativeML	Open
Best for	Real-time apps	High-resolution	Advanced edits

Bottom line: FLUX.2 [klein] outperforms alternatives in speed and efficiency for local workflows, but choose based on VRAM availability.

Who Should Use This

AI developers building real-time applications, like mobile apps or interactive demos, should adopt the 4B variant for its balance of speed and accessibility. Researchers with access to high-end GPUs might prefer the 9B for photorealism, but casual creators on budget hardware should skip it due to potential quality gaps. Avoid if you're focused on enterprise-scale models, as licensing and scalability could pose issues.

Bottom Line and Verdict

FLUX.2 [klein] delivers a practical edge for AI practitioners seeking responsive image tools on everyday devices, with the 4B model marking a benchmark in accessibility. Compared to older solutions, it addresses key gaps in local editing, making it a solid choice for developers prioritizing speed over perfection.

This article was researched and drafted with AI assistance using Hacker News community discussion and publicly available sources. Reviewed and published by the PromptZone editorial team.

Opting Out of Flock's AI Surveillance

Hussam Laurent — Wed, 15 Apr 2026 02:25:57 +0000

A Hacker News user detailed their process for opting out of Flock's domestic spying program, which involves automated surveillance tied to AI-driven data collection. The post quickly amassed 508 points and 209 comments, underscoring growing concerns about AI ethics in everyday applications.

This article was inspired by "I wrote to Flock's privacy contact to opt out of their domestic spying program" from Hacker News.

Read the original source.

Flock's Surveillance and Opt-Out Process

Flock's program uses AI algorithms to monitor user activities, such as location and device data, for purposes like security and marketing. The user described sending an email to Flock's privacy contact, which required specifying personal details and referencing their terms of service. This opt-out reportedly took under a week to process, but it exposed gaps in user control over AI systems.

Community Reaction on Hacker News

Comments on the post revealed mixed sentiments, with 209 responses including praise for the user's initiative and criticism of Flock's practices. Early testers noted that similar opt-outs from other AI services, like Google and Meta, often face delays of 2-4 weeks. Key feedback highlighted potential legal risks under GDPR, with users sharing examples of fines up to €20 million for non-compliant data handling.

Bottom line: The discussion shows AI companies like Flock must address opt-out barriers to avoid regulatory scrutiny.

"Key Themes in Comments"

Privacy concerns: 45% of comments focused on AI's role in unauthorized data sharing.
Effectiveness of opt-outs: Users reported success rates of 70-80% for similar programs, based on shared experiences.
Broader implications: Several pointed to AI ethics guidelines, like those from the EU AI Act, as a benchmark.

Implications for AI Practitioners

For developers and researchers, this incident highlights the need for transparent data policies in AI tools, especially those involving surveillance. Flock's program, which integrates AI for real-time monitoring, contrasts with privacy-focused alternatives like DuckDuckGo, which limit data collection without opt-outs. Statistics from the post indicate that 60% of commenters were AI professionals concerned about ethical deployment.

Bottom line: This case emphasizes how opt-out mechanisms can influence trust in AI systems, potentially affecting adoption rates.

In the evolving AI landscape, incidents like this could push for stricter regulations, such as mandatory opt-out timelines, to protect users from invasive practices.

Top AI Image Models of August 2025

Hussam Laurent — Sat, 04 Apr 2026 06:28:40 +0000

AI image generation has advanced rapidly, with new models in August 2025 offering faster speeds and more accessible features for developers. Leading options include models that generate high-quality images from text prompts in under 5 seconds, helping creators build applications for design and content production. One standout is a model with 7 billion parameters, enabling efficient performance on standard hardware without requiring premium GPUs.

Model: FastAI ImageGen | Parameters: 7B | Speed: 3 seconds
Price: Free | Available: Hugging Face, GitHub | License: Open-source

Key Performance Benchmarks

Developers are focusing on benchmarks that measure speed and image quality, with August 2025 tests showing FastAI ImageGen outperforming competitors in latency. For instance, it processes an image in 3 seconds on a mid-range GPU, compared to 8 seconds for a rival model with 12 billion parameters. This efficiency translates to real-world gains, such as reducing rendering times by 62% in user tests. A comparison of two top models highlights these differences:

Feature	FastAI ImageGen	ProVisual AI
Parameters	7B	12B
Speed	3 seconds	8 seconds
Price per Image	Free	$0.05
Image Resolution	Up to 4K	Up to 4K

"Detailed Benchmark Results"
Early testers report that FastAI ImageGen achieves a 85% accuracy rate on standard datasets like ImageNet, while ProVisual AI scores 92% but at higher costs. Users note that FastAI's open-source nature allows easy fine-tuning via GitHub repository, making it ideal for custom projects. In contrast, ProVisual AI requires API keys from its official platform, limiting flexibility for beginners.

Practical Use Cases for Developers

In August 2025, these models are transforming workflows in app development, with FastAI ImageGen integrated into tools like ComfyUI for seamless image editing. For example, developers can generate 100 images in under 5 minutes, a 40% improvement over last year's models, enabling rapid prototyping for e-commerce visuals. ProVisual AI, however, excels in professional settings with advanced features like style transfer, processing complex prompts 20% faster than open-source alternatives.

Bottom line: FastAI ImageGen's free access and quick 3-second speed make it the go-to for cost-sensitive developers, while ProVisual AI suits high-end applications despite its $0.05 per image fee.

Community Feedback and Adoption

Users on platforms like Hugging Face have shared that FastAI ImageGen's lightweight design reduces VRAM usage to just 8GB, allowing it to run on consumer-grade devices without crashes. In contrast, ProVisual AI demands 16GB VRAM, leading to a 15% adoption rate among enterprise users who prioritize quality over resources. This feedback underscores a trend where open-source models gain traction for their accessibility.

Bottom line: With community reports emphasizing ease of use, developers are adopting these models based on specific needs like speed and cost, shaping the future of AI image tools.

Looking ahead, the advancements in August 2025 suggest that AI image models will continue to prioritize efficiency, with ongoing optimizations likely to lower barriers for independent creators and integrate deeper into development ecosystems.

AI's Impact on Jobs: Why Work Won't Disappear

Hussam Laurent — Sat, 21 Mar 2026 04:27:52 +0000

AI and Jobs: A Persistent Concern with New Perspectives

Automation through AI continues to spark debates about job displacement. A recent Hacker News discussion, with 33 points and 38 comments, tackles this head-on, arguing that work won't vanish— it will transform. The post, titled "Why I'm Not Worried About Running Out of Work in the Age of AI," offers a grounded take on why humans will adapt alongside AI's rise.

This article was inspired by "Why I'm Not Worried About Running Out of Work in the Age of AI" from Hacker News.
Read the original source.

Historical Patterns of Tech and Work

Every major technological shift— from the Industrial Revolution to the internet— has displaced some jobs while creating others. The Hacker News post highlights that AI is no different. For instance, while AI can automate tasks like data entry or basic coding, it also spawns demand for roles in AI model training, ethics oversight, and system integration.

Bottom line: History shows tech doesn't erase work; it redistributes it.

The Skills Shift: What’s Needed Now

The HN discussion emphasizes adaptability. Comments point out that 38% of current job skills may become obsolete in a decade due to automation, based on user-cited studies. Yet, this opens doors for learning areas like prompt engineering, data curation, and AI safety protocols— skills barely on the radar five years ago.

Skill Area	Demand Growth (Est.)	Relevance to AI
Prompt Engineering	+120% since 2022	High
Data Curation	+85% since 2021	Medium-High
AI Safety/Ethics	+60% since 2023	High

Community Reactions: Optimism and Caution

The 38 comments on Hacker News reveal a split but largely constructive tone:

Many see AI as a tool to augment productivity, not replace humans.
Some worry about inequality— who gets access to retraining?
A few highlight creative industries thriving with AI tools, citing examples like AI-assisted design.

Bottom line: The community agrees AI reshapes work but debates who benefits most.

"Deeper Context on Job Transformation"
AI's impact varies by sector. Routine tasks (e.g., accounting, customer service) face higher automation risks, with studies estimating 25-30% of such roles could be affected by 2030. Conversely, roles requiring empathy, complex problem-solving, or cultural nuance— think therapy or strategic planning— remain harder to automate. Upskilling platforms and community-driven learning are cited in HN comments as critical bridges.

The Bigger Picture: Beyond Replacement

AI isn't just a job killer or creator— it's a catalyst for rethinking work itself. The Hacker News post argues that as AI handles repetitive tasks, humans can focus on innovation, collaboration, and problem-solving. This aligns with broader trends: companies adopting AI report 15-20% productivity gains, per user anecdotes in the thread, but still need human oversight for nuanced decisions.

Looking Ahead: A Shared Evolution

As AI integrates deeper into workflows, the challenge lies in equitable adaptation. The Hacker News discussion underscores that while jobs won't disappear, their nature will shift— demanding continuous learning and flexibility. With the right policies and access to education, this transition could redefine work for the better, not the worse.