Hacker News

Article URL: https://www.digitvibe.com/ai-agents-and-the-rise-of-judgment-powered-software/

Comments URL: https://news.ycombinator.com/item?id=47044161

Points: 1

# Comments: 0

Article URL: https://www.wallacecorp.ai/content

Comments URL: https://news.ycombinator.com/item?id=47044157

Points: 1

# Comments: 2

Let's say that I'm a rich mathematician (that would be nice) and want to sponsor some computation that takes a certain gargantuan number N of floating or integer operations, and it can be parallelized easily in the cloud, and my programmers have come up with a reasonably efficient implementation, which I benchmark with some smaller-scale tests. The I/O and storage costs are low; the bulk of the cost is going to be pure computation.

Now I'm ready to get out my checkbook.

What factors influence the cost of such a computation in today's cloud computing market?

How does the price scale with N and the rate at which I want to complete the problem? (for example, if it takes 1000 AWS instances 10 months or 10,000 AWS instances 1 month, am I going to pay the same? I'm assuming the length of time to run this is less than a year, so not long enough that the cost would decrease significantly during the computation.)

Are there aspects of flexibility that would decrease the price? (example: the cloud servers can put my computation on hold or throttle the CPU anytime they want in the short term, as long as the average rate my computation is running in any given day is, say, more than 80% of full-speed)

Lenstra used the term "dollardays" to describe the cost of breaking cryptographic keys as something that scales with the capital cost of the computing equipment (40 million dollardays = 40 days on $1 million worth of computers, or 1 day on $40 million worth of computers) but with cloud computing it seems more like it would be a piecework pricing structure.

Comments URL: https://news.ycombinator.com/item?id=47044151

Points: 1

# Comments: 0

Article URL: https://www.sciencedirect.com/science/article/pii/S2467981X26000107

Comments URL: https://news.ycombinator.com/item?id=47044140

Points: 1

# Comments: 0

Article URL: https://robertkarl.net/blog/articles/grug-meets-his-match-or-grug-claude-and-big-snap-man/index.html

Comments URL: https://news.ycombinator.com/item?id=47044125

Points: 1

# Comments: 0

Article URL: https://kr-asia.com/chinas-cxmt-and-ymtc-to-massively-expand-memory-output-amid-global-crunch

Comments URL: https://news.ycombinator.com/item?id=47044086

Points: 1

# Comments: 1

Hi HN,

I’ve been working on SmartKNN, a nearest-neighbor system designed specifically for production deployment rather than academic experimentation.

The goal was not to slightly tweak classical KNN, but to restructure it into a deployable, latency-aware system while preserving interpretability.

What it does differently

Traditional KNN is simple and interpretable, but in practice it struggles with:

Inference latency as datasets grow

Equal treatment of all features

Fixed distance metrics

Unpredictable performance under load

SmartKNN addresses these issues through:

1. Learned Feature Weighting

Feature importance is learned automatically and incorporated into the distance computation. This reduces noise and improves neighbor quality without manual tuning.

2. Adaptive Distance Behavior

Distance computation adapts to learned feature relevance instead of relying on a fixed metric like plain Euclidean.

3. Backend Selection

SmartKNN supports both brute-force and approximate nearest-neighbor strategies.

Small datasets → brute-force

Larger datasets → approximate candidate retrieval

Approximate search is used only to retrieve candidates. Final prediction always uses the learned distance function.

4. CPU-Focused Design

The system is optimized for predictable CPU inference performance rather than GPU-heavy workflows. The focus is stable latency characteristics suitable for production workloads.

5. Unified API

Supports both classification and regression through a scikit-learn compatible interface.

Performance

On structured/tabular datasets with strong local structure, SmartKNN achieves competitive accuracy against tree-based models.

It does not aim to replace tree models or neural networks universally. It performs best where neighborhood structure is meaningful and interpretability is desired.

Limitations

- Requires dataset to remain in memory - High-dimensional dense data can still challenge nearest-neighbor methods - No online/incremental updates yet - Backend preparation adds setup time for large datasets

Project Status

- Public release: 0.2.2 - Stable API - Open source - CPU-optimized core Repository: https://github.com/thatipamula-jashwanth/smart-knn I’d appreciate feedback, especially from people who have deployed nearest-neighbor systems in production.

Thanks.

- Jashwanth

Comments URL: https://news.ycombinator.com/item?id=47044081

Points: 1

# Comments: 3

Article URL: https://pastebin.com/fUP7PdMF

Comments URL: https://news.ycombinator.com/item?id=47044062

Points: 4

# Comments: 1

Article URL: https://www.foreignaffairs.com/united-states/ai-trilemma

Comments URL: https://news.ycombinator.com/item?id=47044058

Points: 4

# Comments: 1

Article URL: https://confect.dev/getting-started/introduction

Comments URL: https://news.ycombinator.com/item?id=47044055

Points: 1

# Comments: 0

I built this game starting over the Christmas break - you are a solarpunk graffiti dog trying to dominate a futuristic city with your pixel tag!

Comments URL: https://news.ycombinator.com/item?id=47044052

Points: 1

# Comments: 0

Article URL: https://apps.apple.com/kr/app/%EB%AC%B4%EB%A3%8C-%EC%A6%9D%EB%AA%85%EC%82%AC%EC%A7%84-%EC%83%9D%EC%84%B1%EA%B8%B0/id6757347352

Comments URL: https://news.ycombinator.com/item?id=47044048

Points: 1

# Comments: 5

Hey HN,

I've been building CoolWulf AI (https://coolwulfai.com), a self-hosted personal AI assistant. After seeing OpenClaw blow up, I wanted to share what I've been working on — a different approach to the same problem.

*Why I built this:*

I tried OpenClaw and found the Node.js/TypeScript stack heavy for what's essentially a local agent. pnpm, Node 22+, React — lots of moving parts. I wanted something that's a single binary, zero runtime dependencies, and feels native on macOS. So I built it in Go.

*How it's different from OpenClaw:*

- *Single binary, no runtime needed.* Download, set your API key, run. No Node.js, no pnpm, no build step. One ~100 MB binary. - *Built in Go.* Fast startup, low memory footprint, compiles to a native executable. No garbage collector pauses from a JS runtime sitting in the background. - *macOS-native integrations.* Deep AppleScript-based control of Apple Notes, Reminders, Calendar, Terminal.app, and WeChat desktop. These aren't browser hacks — they use the native accessibility and scripting APIs. - *WeChat support.* This was a big one for me. WeChat desktop on macOS is a native Cocoa/Qt app with a readable accessibility tree. CoolWulf can read messages, send messages, search contacts, and navigate chats — all via AX APIs and CGEvent. I haven't seen another AI agent do this. - *Simpler setup.* Web-based first-time wizard. Configure your LLM provider, connect Gmail/Calendar via OAuth, enable messaging connectors — all from the browser. No terminal wizards, no YAML files.

*What it does:*

- 20+ LLM providers (OpenAI, Claude, Gemini, DeepSeek, Groq, Ollama, local models via vLLM/LM Studio) - Messaging: WhatsApp, Teams, Telegram, Slack, WeChat - Email: Gmail and Yahoo Mail with full OAuth - Calendar: Google Calendar + Apple Calendar - Browser automation: Chrome CDP + Playwright via MCP - Task management: org-mode style with scheduled tasks, cron jobs, automatic execution - Persistent memory: SQLite + vector embeddings for semantic search across conversations - Background heartbeat: runs 24/7, monitors your systems, executes due tasks, sends alerts - Web dashboard for chat, tasks, scheduled jobs, and settings

*Architecture choices:*

Go was the right call. The binary compiles in seconds, cross-compiles trivially, and the concurrency model (goroutines for heartbeat, browser sessions, connector polling) maps perfectly to an always-on agent. SQLite with vector extensions (sqlite-vec) gives us semantic memory without running a separate vector DB.

Try it: https://coolwulfai.com

Happy to answer questions about the Go implementation, WeChat automation, or the macOS accessibility approach.

Comments URL: https://news.ycombinator.com/item?id=47044036

Points: 1

# Comments: 1

I built UI-Me because Streamlit felt too heavy and Click/Argparse felt too limiting for quick automation scripts.

It’s a minimalist library that maps your Python functions to a clean web dashboard instantly.

The Essentials:

- One Decorator: Add @ui_enabled to any function to generate a UI form. - No Frontend Bloat: Built with vanilla HTML/JS and Flask. No React, no NPM, no complex build steps. - Smart Inputs: It uses type hints to build forms (supporting strings, numbers, lists, and nested JSON). - Global State: A dedicated sidebar to view and update global variables (like API keys) on the fly. - Rich Output: Function results are displayed in a searchable, collapsible JSON viewer.

It’s essentially "Swagger UI" for your internal scripts. Perfect for when you need a control panel but don't want to spend an hour building one.

Comments URL: https://news.ycombinator.com/item?id=47044026

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=mUmlv814aJo

Comments URL: https://news.ycombinator.com/item?id=47043929

Points: 1

# Comments: 0

Microsoft learning site (look under “Git flow”: https://learn.microsoft.com/en-us/training/modules/introduction-to-github/3-components-of-github-flow

Original: https://nvie.com/posts/a-successful-git-branching-model/

Comments URL: https://news.ycombinator.com/item?id=47043915

Points: 1

# Comments: 0

Article URL: https://www.bbc.co.uk/news/articles/cr5l6gnen72o

Comments URL: https://news.ycombinator.com/item?id=47043885

Points: 1

# Comments: 0

Article URL: https://www.science.org/content/article/octopuses-fall-rubber-arm-illusion-just-us

Comments URL: https://news.ycombinator.com/item?id=47043882

Points: 1

# Comments: 0

I built this after seeing too many sites lose Google Ads conversion data due to broken Consent Mode v2 setup.

Free scanner checks GTM/GA4/Ads + consent signals in 60 seconds. No signup required.

Example scan: https://tagintegrity.lovable.app/results/347d5a06-541d-4d6c-...

Would love feedback on what would make this more useful!

Comments URL: https://news.ycombinator.com/item?id=47043867

Points: 1

# Comments: 0

Article URL: https://on-the-same.page

Comments URL: https://news.ycombinator.com/item?id=47043865

Points: 1

# Comments: 1