frontpage.
newsnewestaskshowjobs

Open Source @Github

fp.

Why attacking your competitors online is dumb

https://posthog.com/blog/why-attacking-competitors-is-dumb
1•mmarian•38s ago•0 comments

AI's Brokenomics

https://www.wheresyoured.at/brokenomics/
1•sarmike31•41s ago•0 comments

Using Task Graph Caching to Accelerate TVM Code Generation

https://dl.acm.org/doi/10.1145/3810246
1•matt_d•1m ago•0 comments

Complex-If and Beyond: Expert Rubrics for RLVR [pdf]

https://cdn.prod.website-files.com/68dc970bd6e945ea3fb0f426/6a24113dce0f59637d14881a_complex_if.pdf
1•gk1•1m ago•0 comments

Republican Gov. Mike DeWine wants Ohio to abolish the death penalty

https://apnews.com/article/death-penalty-ohio-dewine-6210d7fbcecde9fe88657a76521e90fe
1•danso•1m ago•0 comments

Write code that is easy to delete, not easy to extend. (2013)

https://programmingisterrible.com/post/139222674273/write-code-that-is-easy-to-delete-not-easy-to
1•downbad_•2m ago•0 comments

Infinitely Morphing Tesselated Ribbon

https://sand-morph.up.railway.app/a-coil-of-quiet-scales
1•echohive42•2m ago•0 comments

DeepSeek V4 Pro at 5% the cost of Claude – what it takes to close the gap

https://howardchen.substack.com/p/deepseek-v4-pro-at-5-the-cost-of
1•coolwulf•5m ago•0 comments

Coffee Through Divine Intervention

https://alieniloquy.bearblog.dev/coffee-through-divine-intervention/
1•speckx•5m ago•0 comments

Optimizing a C collision detection 100x with an LLM

https://twitter.com/mike_acton/status/2066778535902298405
1•stephc_int13•6m ago•0 comments

I know you're arguing with your wife over 'one more prompt,' and here's why

https://hayredd.in/blog/accidental-gamification-vibe-coding
2•devneeddev•7m ago•0 comments

Show HN: Tally Marks – an app for counting with a handwritten look & feel

https://tallymarks.app/
1•bcye•7m ago•0 comments

wolfSSL releases a new product; wolfHAL a light hardware abstraction layer

https://github.com/wolfSSL/wolfHAL
1•aidangarske•9m ago•0 comments

FP8 GEMM Optimization on AMD CDNA4 Architecture

https://rocm.blogs.amd.com/software-tools-optimization/cdna4-gemm-kernels/README.html
1•skidrow•9m ago•0 comments

Deep Dive into 4-Wave Interleave FP8 GEMM

https://rocm.blogs.amd.com/software-tools-optimization/4wave-fp8gemm/README.html
1•skidrow•10m ago•0 comments

Show HN: I'm 15, built an AI that watches your screen and acts before you ask

https://github.com/Helmus101/weave
2•anqer•10m ago•0 comments

Flexible Rate Limit Resets for Codex (bank rate limit resets)

https://community.openai.com/t/flexible-rate-limit-resets-for-codex-and-a-method-to-get-a-reset/1...
3•embedding-shape•12m ago•0 comments

A Metacircular Interpreter in Rhombus

https://github.com/racket/rhombus/blob/master/rhombus/rhombus/tests/example-interp.rhm
2•spdegabrielle•15m ago•1 comments

AI is potentially a Dunning-Kruger effect amplifier

https://twitter.com/i/status/2066825204207091926
3•binyu•17m ago•0 comments

We built an agent that runs our AI data platform

https://encord.com/blog/merlin-encord-mcp-agentic-intelligence/
3•ulrikhansen54•19m ago•0 comments

Databricks Launches LTAP: A Unified OLAP/OLTP Data Architecture

https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-tr...
4•thehaikuza•19m ago•1 comments

Predicting model behavior before release by simulating deployment

https://openai.com/index/deployment-simulation/
3•0xedb•20m ago•0 comments

The feedback loops behind Kubernetes

https://planetscale.com/blog/the-feedback-loops-behind-kubernetes
2•CSDude•21m ago•0 comments

Hecate, Hardened Osint Platform

https://github.com/synchancybersecurity/Hecate
2•enkimecca•22m ago•0 comments

U.S. pulling ocean sensors a 'shock' for Canadian research as El Niño nears

https://www.timescolonist.com/local-news/us-pulling-ocean-sensors-a-shock-for-canadian-research-a...
89•ResearchAtPlay•22m ago•19 comments

PHP Through a Screen Reader: Small Syntax Choices That Matter

https://thephp.foundation/blog/2026/06/16/php-through-a-screen-reader-small-syntax-choices-that-m...
1•itafroma•22m ago•0 comments

Noctua introduces NL-LC1 all-in-one liquid coolers

https://www.noctua.at/en/news/noctua-introduces-nl-lc1-all-in-one-liquid-coolers
5•georgs_•23m ago•0 comments

Rabbit Hole: The Lorem Ipsum Mystery [video]

https://www.youtube.com/watch?v=kL1PDqzqhM4
1•luizfzs•24m ago•0 comments

Show HN: Next.js boilerplate with Better Auth, PostgreSQL and Shadcn/UI

https://github.com/mmilanovic4/forge
1•mmilanovic4•24m ago•0 comments

Tyler Cowen: A Dangerous Turn in AI Regulation

https://www.thefp.com/p/tyler-cowen-a-dangerous-turn-in-ai
1•paulpauper•24m ago•0 comments
Open in hackernews

Last month 10k apps were built on our platform – here's what we learned

5•jonathanhar•1y ago
Hey all, Jonathan here, cofounder of Fine.dev

Over the last month alone, we've seen more than 10,000 apps built on our product, an AI-powered app creation platform. That gave us a pretty unique vantage point to understand how people actually use AI to build software. We thought we had it pretty much figured out, but what we learned changed our thinking completely.

Here are the three biggest things we learned:

1. Reducing the agent's scope of action improves outcomes (significantly)

At first, we thought “the more the AI can do, the better.” Turns out… not really. When the agent had too much freedom, users got vague, bloated, or irrelevant results. But when we narrowed the scope the results got shockingly better. We even stopped using tool calls almost all together. We never expected this to happen, but here we are. Bottom line - small, focused prompts → cleaner, more useful apps.

2. The first prompt matters. A lot.

We’ve seen prompt quality vary wildly. The difference between "make me a productivity tool" and "give me a morning checklist with 3 fields I can check off and reset each day" is everything. In fact, the success of the app often came down to just how detailed was that first prompt. If it was good enough - users could easily make iterations on top of it until they got their perfect result. If it wasn't good enough, the iterations weren't really useful. Bottom line - make sure to invest in your first request, it will set the tone for the rest of the process.

3. Most apps were small + personal + temporary.

Here’s what really blew our minds: People weren't building startups / businesses. They were building tools for themselves. For this week. For this moment. A gift tracker just for this year's holidays, a group trip planner for the weekend, a quick dashboard to help their kid with morning routines, a way to RSVP for a one-time event. Most of these apps weren’t meant to last. And that's what made them valuable.

This led us to a big shift in our thinking:

We’ve always thought of software as product or infrastructure. But after watching 10,000 apps come to life, we’re convinced it’s also becoming content: fast to create, easy to discard, and deeply personal. In fact, we even released a Feed where every post is a working app you can remix, rebuild, or discard.

We think we're entering the age of disposable software, and AI app builders is where that shift comes to life.

Also happy to answer questions about what we learned from the first 10K apps AMA style.

Comments

kingkongjaffa•1y ago
> We think we're entering the age of disposable software, and AI app builders is where that shift comes to life.

This is a fascinating thought. I wonder if there's some disconnect between good design and the immediacy of building something that solves exactly the thing you need to solve at the time.

What I mean is, when you first build something, it probably does what users need, but there's always some rough edges. Frankly out of 10,000 throwaway apps built, I'm going to guess probably less than 10 have been built with good design and taste.

It's like the difference between a TODO MVP toy app to track tasks, vs something like Linear which is beautifully designed.

Both probably have their place I think.

For my work I'm not sure I want my tools to be so discardable personally. I want to use predictable, well designed tools that have had their rough edges sanded down through iteratively reducing the micro-frictions I have in my day to day job. Behind every great product experience there's usually someone obsessing over a specific pain point and motivated to make something great.

Toy throwaway apps can't replace human thinking time and experience using a tool over months and years.

For personal and one time problems, toy apps can absolutely get the job done, and most people are willing to overlook the rough edges.

tomcam•1y ago
> When the agent had too much freedom, users got vague, bloated, or irrelevant results.

Listen, pal: I was vague and bloated long before you released your little platform!