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I am a developer. I have been building stuff for the last 13 years. One common challenge in all my projects is compressing assets to make them as small as possible. I used to end up using online compressors, but this has a lot of privacy concerns.

So, I finally set down for couple of weeks and built this simple app. I can just drag and drop assets in it and it shrinks them. It currently supports around 25 file types.

I built a lot of iOS apps but this is the first time building a macOS app, so any feedback is welcome.

Comments URL: https://news.ycombinator.com/item?id=47000541

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Article URL: https://twitter.com/TheDealMakerGuy/status/2021980347752476910

Comments URL: https://news.ycombinator.com/item?id=47000538

Points: 2

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Article URL: https://github.com/alibaba/zvec

Comments URL: https://news.ycombinator.com/item?id=47000535

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Article URL: https://goaccess.io/release-notes

Comments URL: https://news.ycombinator.com/item?id=47000269

Points: 1

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Three of the security defects are high-severity flaws, two of which were found and reported by Google.

The post Chrome 145 Patches 11 Vulnerabilities appeared first on SecurityWeek.

Article URL: http://karpathy.github.io/2026/02/12/microgpt/

Comments URL: https://news.ycombinator.com/item?id=47000263

Points: 1

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Article URL: https://tomquirk.me/reflecting-on-my-ai-adoption-timeline

Comments URL: https://news.ycombinator.com/item?id=47000256

Points: 1

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Article URL: https://www.theguardian.com/technology/2026/feb/11/big-ai-job-swap-white-collar-workers-ditching-their-careers

Comments URL: https://news.ycombinator.com/item?id=47000214

Points: 1

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Article URL: https://bsky.app/profile/thekinsie.com/post/3mep77kgpps2r

Comments URL: https://news.ycombinator.com/item?id=47000210

Points: 1

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I stopped my CC Max plan a few months ago, but I'm trying it again for fun after seeing their $30 billion series G or whatever.

It just doesn't work. I'm trying to build a simple tool that will let me visualize grid layouts.

It needs to toggle between landscape/portrait, and implement some design strategies so I can see different visualizations of the grid. I asked it to give me a slider to simulate the number of grids.

1st pass, it made something, but it was squished. And toggling between landscape and portrait made it so it squished itself the other way so I couldn't even see anything.

2nd pass, syntax error.

3rd try I ask it to redo everything from scratch. It now has a working slider, but the landscape/portrait is still broken.

4th try, it manages to fix the landscape/portrait issue, but now the issue is that the controls are behind the display so I have to reload the page.

5th try, it manages to fix this issue, but now it is squished again.

6th try, I ask it to try again from scratch. This time it gives me a syntax error.

This is so frustrating.

Comments URL: https://news.ycombinator.com/item?id=47000206

Points: 1

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Hi HN — I built a small web app that scans a list of tickers across multiple timeframes and flags potential overbought/oversold mean‑reversion setups using a regression-channel Z‑score.

Live MVP: https://zcoreai.onrender.com/

What it does:

Pick tickers + timeframes, then run a scan

Outputs a matrix of signals (simple labels now; “expert” view shows exact values of Z-Score)

Why: I wanted something fast to answer “what’s oversold / overbought right now?” without opening all charts on every timeframes on TradingView.

Notes / current state:

- Early MVP, UI is intentionally minimal

I’d love feedback on:

- Whether the output is understandable/useful

- Which features you’d want next (alerts, presets, exports, etc.)

- Any obvious UX issues or missing pieces for a tool like this

Happy to answer questions and share implementation details if people are interested.

Comments URL: https://news.ycombinator.com/item?id=47000182

Points: 1

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Article URL: https://github.com/bountyyfi/invisible-prompt-injection

Comments URL: https://news.ycombinator.com/item?id=47000173

Points: 1

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Article URL: https://howcooked.me/

Comments URL: https://news.ycombinator.com/item?id=47000170

Points: 1

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Article URL: https://css-doodle.com/

Comments URL: https://news.ycombinator.com/item?id=47000164

Points: 2

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Article URL: https://programmingappliedai.substack.com/p/hld-design-real-time-monitoring-system

Comments URL: https://news.ycombinator.com/item?id=47000161

Points: 1

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Article URL: https://ulavu.lovable.app

Comments URL: https://news.ycombinator.com/item?id=47000148

Points: 1

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Abstract

The last five years have seen explosive progress in large language models (LLMs) — exemplified by systems such as ChatGPT and GPT-4 — which deliver broad capabilities but at heavy computational, latency, privacy, and cost budgets. In parallel, a renewed research and engineering focus on Small Language Models (SLMs) — compact, task-optimized models that run on-device or on constrained servers — has produced techniques and models that close much of the gap while enabling new applications (on-device inference, embedded robotics, low-cost production). This article/review compares SLMs and LLMs across design, training, deployment, and application dimensions; surveys core compression methods (distillation, quantization, parameter-efficient tuning); examines benchmarks and representative SLMs (e.g., TinyLlama); and proposes evaluation criteria and recommended research directions for widely deployable language intelligence. Key claims are supported by recent surveys, empirical papers, and benchmark studies.

1. Introduction & Motivation

Large models (billions to hundreds of billions of parameters) have pushed capabilities for zero-shot reasoning, instruction following, and multi-turn dialogue. However, their deployment often requires large GPUs/TPUs, reliable cloud connectivity, and high inference cost — constraints that hinder low-latency, private, and offline applications (mobile apps, robots, IoT). Small Language Models (SLMs) are intentionally compact architectures (ranging from ~100M to a few billion parameters) or compressed variants of LLMs designed for on-device or constrained-server inference. SLMs are not merely “smaller copies” of LLMs: the field now includes architecture choices, fine-tuning regimes, and tooling (quantization, distillation, pruning) that produce models tailored for specific constraints and use-cases. Recent comprehensive surveys document this growing ecosystem and its practical impact.

2. Definitions & Taxonomy

LLM (Large Language Model): Very large transformer-based models (≥10B params typical) trained on massive corpora. Strengths: generality, emergent capabilities. Weaknesses: cost, latency, privacy exposure.

SLM (Small Language Model): Compact models (≈10⁷–10⁹+ params) or aggressively compressed LLM variants that aim for high compute/latency efficiency while retaining acceptable task performance. SLMs include purpose-built small architectures (TinyLlama), distilled students (DistilBERT style), and heavily quantized LLMs.

Compression & Efficiency Methods: Knowledge distillation, post-training quantization (GPTQ/AWQ/GGUF workflows), pruning, low-rank/adapters (LoRA), and mixed-precision training.

Comments URL: https://news.ycombinator.com/item?id=47000145

Points: 1

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Article URL: https://www.codespeak.dev/

Comments URL: https://news.ycombinator.com/item?id=47000141

Points: 1

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Article URL: https://dreamhomestoreblog.wordpress.com/2026/02/11/bed-frames-that-work-harder-in-small-bedrooms/

Comments URL: https://news.ycombinator.com/item?id=47000127

Points: 1

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Article URL: https://revelry.co/insights/software-estimation-everything-takes-longer/

Comments URL: https://news.ycombinator.com/item?id=47000124

Points: 1

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