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The article discusses the importance of recognizing software development as a collaborative effort, where the goal is to address shared frustrations and challenges, rather than a competitive endeavor. It emphasizes the need for empathy, communication, and a focus on solving problems for users, rather than engaging in unnecessary comparisons or rivalries.

The article explores the role of artificial snow in the ski industry, examining its potential benefits and drawbacks. It discusses the sustainability and long-term viability of relying on artificial snow to maintain ski slopes as natural snowfall declines due to climate change.

The article discusses a project by a software engineer who found and reported over 200 kernel bugs in the Linux kernel within a 30-day period, highlighting the importance of thorough code review and testing to identify and address vulnerabilities in critical software systems.

I'm not a developer — I'm the person who came up with this idea and built an interactive prototype with Claude's help. I'm posting here because this is exactly the kind of project that needs real engineers to take it somewhere.

The idea: a declare-ai.json file that any creator, platform, or AI tool can publish alongside content — declaring what percentage was written, coded, illustrated, or researched by AI versus humans, which tools were used, and who the human contributors are. A lightweight embeddable widget displays it as a collapsible pie chart. A browser extension auto-detects it on any page. A community forum handles disputes.

Think Creative Commons, but for AI provenance. Or a nutrition label, but for intelligence.

The demo is a full interactive developer briefing — architecture diagram, JSON schema, forum mockup with a real dispute thread example, tech stack recommendation, and phased roadmap. The widget on the page declares itself.

I've gifted it as MIT. I'm looking for developers who want to own this with me.

GitHub: https://github.com/Declare-AI/declare-ai Live demo: https://declare-ai.github.io/declare-ai/declare-ai-devteam.h...

Genuinely open to all feedback — including "this already exists and here's why it won't work."

Apple is expected to maintain its dominance in the European smartphone market, with projected sales of over 100 million iPhones in the region by 2025, representing a new sales record. The forecast reflects Apple's continued popularity and growth in the European smartphone landscape.

Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard.

Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement.

Three modes:

Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc

One line to integrate: npm install @limits/js

our website: https://limits.dev

our docs: https://docs.limits.dev

We've processed 30,000+ policy checks across 16 teams. Would love feedback from anyone who's built something like this internally."

ResumeCV.pro is a professional online platform that helps individuals create and optimize their resumes and CVs. The website offers a range of tools, templates, and expert guidance to enhance the quality and effectiveness of job application materials.

The article provides an outlook on the data center industry, highlighting the growing demand for data centers and the challenges they face, including energy consumption, sustainability, and the need for improved infrastructure and technology to support the increasing data processing and storage requirements.

Western Digital has sold out its entire 2026 hard drive production capacity, as AI centers scramble to secure storage for their rapidly expanding data needs, highlighting the growing demand for data storage solutions.

The article explores the resurgence of Ludditism, with a humorous focus on the surprisingly advanced and visually captivating tapestries that modern looms are capable of producing, challenging the traditional perception of Luddites as being opposed to technological progress.

The article provides a detailed guide on how to use the indoor47/summarize-docs project, which is a tool for automatically summarizing documents. It covers the project's features, setup instructions, and examples of how to use the tool to generate concise summaries of text.

Capybara is an open-source web automation framework that allows developers to write tests for web applications. It provides a simple and intuitive API for interacting with web pages, making it easier to create reliable and maintainable tests for web applications.

The article discusses a tool called Secrets, which is a command-line tool for securely managing and sharing sensitive information, such as passwords, API keys, and other secrets, across a team or organization. The tool provides features like encryption, access control, and audit logging to ensure the security and confidentiality of the stored data.

The article argues that the traditional programming career path is becoming less viable, with increasing automation and a shift towards low-code/no-code solutions. It suggests that junior and mid-level engineers should explore alternative career paths, such as becoming product managers, business analysts, or focusing on domain expertise rather than pure coding.

The article explores the concept of intelligence yield, a measure of the efficiency of intelligence gathering and analysis. It discusses the factors that impact intelligence yield, such as information quality, processing capacity, and organizational structures, and how to optimize these elements for better intelligence outcomes.

Hi HN, I'm Donald.

I’m a solo developer building NeoShift BI (and its backend companion, NeoShift ETL).

Enterprise BI tools like PowerBI or Looker are incredibly powerful, but they usually require dedicated data engineering teams to manage. On the other end of the spectrum, dumping a CSV into ChatGPT is great for a quick question but terrible for building reproducible, interactive, and shareable dashboards.

I wanted something in the middle: a lightweight BI tool tailored for indie devs and SMEs that lets you drop in a dataset and get a full dashboard with AI-generated insights in just a few minutes.

The Stack & Architecture:

Infrastructure: Google Cloud

AI Engine: Claude. We don't just use LLMs for text summaries; Claude powers the entire data infrastructure layer. It natively reads and understands your schema to power:

Data Studio Assistant: Automatically finds table relationships and generates complex wide views.

Chat-to-Data: Query your datasets naturally without writing SQL.

AI Chart Builder: Describe what you want to visualize, and the AI constructs the exact chart to drop into your dashboard.

Insight Blocks: Reasons directly over the live data to generate executive summaries and strategic takeaways.

The Demo (Why Startups Fail): To stress-test this, I dropped a Kaggle dataset of 409 startup post-mortems into NeoShift. The AI immediately highlighted that 75% of startups are actually killed by established Giants, not a lack of funding. You can see the generated dashboard and read the AI's analysis here: https://bi.neoshift.ai/#/public/dashboard/startup-failure-pr...

Stress-Test My App (The Open Beta Challenge): I just shipped a new "Public Share Links" feature and I want to test the infrastructure limits. I’m extending our Open Beta to March 27th and running a data storytelling challenge.

If you want to break my app or just visualize some cool data, grab a weird dataset from Kaggle, drop it into NeoShift, and share the link. I'm giving a Free Lifetime Enterprise Account (100GB storage, 5 seats) to the dashboard with the best combined score (unique views + feature usage).

To track the competition, I built a live Leaderboard using NeoShift BI itself (connected to the Google Analytics API). You can check the exact rules and current standings here: https://bi.neoshift.ai/#/public/dashboard/competition-leader...

You can unlock a free share link to test it out with code BETA-320430B4 here: https://bi.neoshift.ai/#/register

I’d love your brutal, unfiltered feedback on the UI, the depth of the Claude integration, and how the system handles whatever weird CSVs you throw at it. Happy to answer any questions about the architecture!

Building the editor I always wanted to use.

Just launched a major update to my custom fork of the Zed editor, focusing on giving the AI agent genuine, long-term capabilities.

What's new: - Persistent Memory: The agent now uses SQLite to remember and recall your project's architecture, patterns, and issues across sessions. No more repeating yourself every morning. - Headless Web Browsing: Integrated a headless Chrome engine. Type /search and the agent will browse the web (even React sites!) to find and synthesize answers directly in your chat panel. - LSP Symbol Search: Upgraded from regex indexing to true, type-aware Language Server integration. - Azure Anthropic & Caching: Natively supports Azure endpoints and enables token-caching UI by default., saves a lot $, prompts are customized to increase prompt caching. - Importantly, complete control the agents system prompts and tool calls.

It’s fast, incredibly capable, and built entirely in Rust. Check out the repo to see it in action: https://lnkd.in/guC9td4M. Binaries notarized for mac!

#Zed #Rust #AI #DeveloperProductivity

No more tmux/Tailscale-type stuff needed now?

The article discusses the growing threat of AI-generated synthetic media, known as deepfakes, and the potential risks they pose in terms of misinformation, fraud, and privacy violations. It explores the technological advancements that have made deepfakes more convincing and accessible, as well as the efforts by technology companies and governments to develop detection methods and regulations to address this emerging challenge.

Seedream 5.0 Lite just dropped. Seedream 5.0 Lite just dropped. If you're curious about the most cost-effective way to run this in a production workflow, I put together a quick breakdown of the features and a price comparison across a few providers.

1. Seedream 5.0 Lite Key Enhancements Here is what stands out in the 5.0 Lite update: - Stronger Feature Consistency: Noticeable jump in facial consistency and detail when using multi-image references. - Detail Preservation: It maintains natural skin tones and postures much better across batch outputs. - Precise Instruction Following: Handles complex camera angles and specific brush-style effects more reliably. - Multimodal Reasoning: You can feed it rough sketches or abstract logic, and it translates them into commercial-ready designs. - Visualizing Complex Data: Great for turning raw data or knowledge sets into clean visuals for presentations. - Broad Use Cases: Fast enough for marketing/E-commerce but high-quality enough for film/game pre-production.

2. Use Case - Design productivity: element and font assets, brand and creative posters, marketing visuals, UI design, social media content, illustration, and commercial photography assets. - UGC play: general photo editing, background changes and color grading, photo stylization, portraits, character merchandise, and playful composites or memes. - Content creation: story and short‑film creation, comics and manga, game content and original characters, plus children’s books, tutorials, and emotional illustrations.

3. My cost on Seedream 4.5 API this week I haven't fully migrated my main project to 5.0 yet, but here’s what I spent running Seedream 4.5 on AtlasCloud.ai over the last 7 days. It could be considered a benchmark. Our team uses ComfyUI to generate e-commerce ad images and n8n to automate the scheduled posting of product visuals. Since AtlasCloud provides native nodes for both tools, we’ve been able to seamlessly integrate it into our existing workflow without any friction. - Total Output: ~2,400 images. - The Setup: Switched from official endpoints to Atlas Cloud API to save overhead. - Total Spent: ~$91.00 (at $0.038/img). - The Math: By staying off the official $0.04/img rate, I’ve been saving consistently. Now that 5.0 Lite is out on Atlas at an even lower entry point, the burn rate is going to drop even further.

4. API Price Comparison (Per Image, USD) - Seedream 5.0 official $0.035 atlas cloud $0.035. - Seedream 4.5 official $0.040 atlas cloud $0.038 fal ai $0.040 wavespeed $0.040. - Nano Banana Pro official $0.139–$0.240 atlas cloud $0.063 fal ai $0.150 wavespeed $0.140. - Qianwen Image Edit Plus official $0.030 atlas cloud $0.021 fal ai $0.030. - FLUX.2 Pro official $0.050 atlas cloud $0.030 fal ai $0.030 wavespeed $0.030. - GPT Image 1.5 official $0.030 fal ai $0.030 wavespeed $0.034.

Final Thoughts If you are just doing 1 or 2 images, official web apps are fine. But if you're building a tool or running a heavy workflow, the price delta adds up fast. I’ve found Seedream 5.0 Lite to be the "sweet spot" for speed vs. cost right now, especially through Atlas if you're trying to keep the burn rate low.

Doppler.js: https://github.com/clocksmith/doppler Live demo: https://d4da.com (bring your own safetensors or use the Gemma samples)

Core problem: for browser-local inference loops, TVM/ORT-style stacks can be hard to inspect and iterate on.

Thesis: - Keep development and runtime build-free (JS + WGSL only) - Make execution choices explicit, benchmarkable, traceable, and profilable

Solution: - Explicit model/kernel-path control - Browser + CLI contract parity (Node must run WebGPU, or use headless browser with WebGPU) - Reproducible phase benchmarks vs Transformers.js (v4) - Artifacts: https://github.com/clocksmith/doppler/tree/main/benchmarks/v...

Request: if this is flawed or pointless, please tell me exactly where or why. If I’m flawed or pointless, well...technical feedback only please =)

The article discusses the Prompt Dump, a Hugging Face Space that provides a collection of prompts for different language tasks and models. The Prompt Dump aims to serve as a resource for users to explore and utilize various prompting techniques to improve their natural language processing applications.