GPT-5
Show HN: A TUI for viewing Android adb logcat logs
Viewing Android logs usually means either drowning in raw `adb logcat` output or opening Android Studio just to use its log viewer. Neither is great if you live in the terminal.<p>LazyLogcat is a TUI built in Go on top of Bubble Tea. It lets you filter by tag/level, search, and tail logs from connected devices — covering most of what Android Studio's logcat viewer offers, without leaving your terminal.<p>Would love feedback on what's missing or what you'd want next.
Show HN: OpenKIWI (Knowledge Integration and Workflow Intelligence)
I've always been passionate about AI and automation so when I first heard about OpenClaw I got excited.
But as a professional software developer for almost 20 years, and who currently leads a DevOps team I found the experience extremely frustrating: constant gateway restarts and "NO_REPLY" responses from my agents when I would simply ask "Are you there?".So after using it for a bit the TLDR is that while I think OpenClaw and all the other AI driven automation tools like
Show HN: CodexBar for Android – Monitor Claude/Codex quotas on your phone
I ported CodexBar (a macOS menu bar app by @steipete) to Android after getting
tired of opening three browser tabs to check whether I'd burned through my quotas.It monitors Claude, Codex (ChatGPT), and Gemini usage in one place — persistent
notification, Quick Settings tile, background refresh, and push alerts on reset.A few notes:
- Uses the same OAuth endpoints the CLI tools rely on, so you extract tokens from
your local CLI config (no separate login flow)
- No backend server. All to
Built a small Postgres tool. Would love some honest feedback
I’ve been working on a small open-source project called Poge: https://github.com/dev-hari-prasad/pogeIt’s a lightweight tool for quickly interacting with PostgreSQL — mainly inspecting tables and running queries without opening heavier tools like pgAdmin.I originally built it for my own workflow when I just wanted something quick to check data or run a query while developing.If anyone here works with Postgres regularly, I’d really appreciate:- Honest feedback
- Feature ideas
Show HN: TerminalNexus – Turn CLI commands into reusable buttons (Windows)
I’m Dan. I built TerminalNexus because I was tired of retyping and hunting down CLI commands I use all the time.At its core, it lets you turn commands (or scripts) into reusable buttons inside a multi-tab Windows terminal. You can organize, categorize, and quickly re-run them without digging through notes or history.It started as a way to manage and reuse commands. I kept adding features I personally needed, and it grew from there.Some of what’s in it now:Command scheduling with output historyAI
Show HN: ContextCache – Cache tool schema KV states, skip 99% of prefill tokens
Every tool-calling LLM request resends the full tool schemas through prefill.
With 50 tools that's ~6,000 tokens reprocessed on every request, for every user,
even though the tools never change.ContextCache compiles tool schemas into a KV cache once and reuses it across all
requests. Only the user query goes through prefill.Results (Qwen3-8B, RTX 3090 Ti):
- 50 tools: 5,625ms → 193ms (29.2x speedup)
- Zero quality degradation (TSA 0.850 matches full prefill exactly)Also includes a CPU-on
Show HN: Lexio – AI-Native PDF Reader (Ollama, Claude, OpenAI, Gemini)
I built Lexio because the standard workflow is broken: copy text from your PDF, switch to a chat window, paste context, explain what you're reading, get an answer, switch back. Repeat forever.The core idea: AI should live inside the reader, not beside it. Select any passage, hit "Ask AI", and get a response grounded in the entire document. But the feature I'm most proud of: you can summarize your entire AI conversation and attach it directly as a comment on the PDF — so your
OpenAI releases GPT-5.3 Instant, a model that's supposed to be "less cringe"
While facing massive public backlash over a new Pentagon deal, OpenAI decided the best course of action was to release a new ...
OpenAI introduces GPT 5.3 Instant for ChatGPT: Check new upgrades and availability details
OpenAI has rolled out GPT 5.3 Instant for ChatGPT, introducing improvements in tone, accuracy, fewer unnecessary refusals and enhanced web-based responses.
OpenAI upgrades ChatGPT with GPT-5.3 Instant model for accuracy
Instant today, an updated version of ChatGPT’s most-used model designed to deliver more accurate answers and improved conversational flow ...
ChatGPT New GPT-5.3 Instant Model Aims to Improve “Everyday Usability”
OpenAI released today a new GPT-5.3 Instant model to address the main shortcomings of its existing GPT-5.2 Instant model.
OpenAI releases GPT-5.3 Instant with fewer refusals and improved web answers
OpenAI releases GPT-5.3 Instant for ChatGPT with fewer refusals, improved web answers, and reduced hallucinations across major benchmarks.
OpenAI says GPT-5.3 Instant will reduce ChatGPT’s ‘cringe’ tone
OpenAI says its new GPT-5.3 Instant model will tone down ChatGPT’s overly reassuring language, aiming to reduce “cringe” responses and deliver more direct, context-appropriate answers after widespread user complaints.
GPT-5.3 Instant cuts hallucinations by 26.8% as OpenAI shifts focus from speed to accuracy
GPT-5.3 Instant reduces hallucinations by 26.8% on web queries and 19.7% on internal knowledge — OpenAI's most-used model now ...
A proxy that cuts LLM API bills by routing simple tasks to cheaper models
Hey HN,Over the last few months, I noticed a massive problem: developers (including me) are lazy. We were sending every single prompt—even basic JSON extractions—to GPT-4o or Claude 3.5 Sonnet, and my API bills were sky rocketingBecause of this I built an AI gateway to fix this. It acts as a drop-in replacement for your OpenAI endpoint. When a request comes in, a tiny, fast classifier scores the prompt's complexity in a few milliseconds. It switches which LLM to use based on it's promp
Show HN: Pencil Puzzle Bench – LLM Benchmark for Multi-Step Verifiable Reasoning
I've been working on applying LLMs to long-context, verifiable problems over the past year, and today I'm releasing a benchmark of 62,000 pencil puzzles across 94 types (sudoku, nonori, slitherlink, etc.). The benchmark also allows for intermediate checks /rule breaks for all varieties at any step.I tested 51 models against a subset (300 puzzles) in two modes: single-shot (output the full solution) and agentic (iterate with verifier feedback).Some results:- Best model (GPT 5.2@xh
Show HN: Stackhaus – A marketplace for AI-built apps (1,204 verified at launch)
My co-founder and I launched Stackhaus publicly today.The problem: generative coding tools (Lovable, Bolt, Cursor, Claude, ChatGPT) have made it genuinely fast to build working software. But the distribution layer for these apps doesn't exist. Most AI-built apps die in a Discord message or a Reddit thread.Stackhaus is a marketplace specifically for AI-generated applications. Every app is verified before listing. Users can browse by category, use case, or the AI/tool that built it. Laun
Show HN: AgentCost – Track, control, and optimize your AI spending (MIT)
Hi HN,
We built AgentCost to solve a problem we kept running into: nobody knows what their AI agents actually cost.
One line wraps your OpenAI/Anthropic client:
from agentcost.sdk import trace
client = trace(OpenAI(), project="my-app")
From there you get a dashboard with cost forecasting, model optimization recommendations, and pre-call cost estimation across 42 models.
What's included (MIT):Python + TypeScript SDKs
Real-time dashboard with 6 views
Cost forecasting (linear, E
Show HN: Yardstiq – Compare LLM outputs side-by-side in your terminal
Hey HN,I built yardstiq because I got tired of the copy-paste workflow for comparing LLM responses when developing apps. Every time I wanted to see how Claude vs GPT vs Gemini handled the same prompt, I'd open three tabs, paste the same thing, and try to eyeball the differences. It's 2026 and we have 40+ models worth considering — that doesn't scale.yardstiq is a CLI tool that sends one prompt to multiple models simultaneously and streams the responses side-by-side in your termina
Show HN: Train a GPT from scratch in the browser – Karpathy's microGPT
Faithful reimplementation of Karpathy's microGPT that runs entirely in a Web Worker. You pick a dataset (YC startups, baby names, dinosaurs, or upload your own), configure the architecture, and watch the loss curve drop in real-time as it trains. Then generate text from your model. Everything runs client-side - back propagation, attention, the whole training loop. The fun technical constraint was keeping the UI responsive while doing matrix math in JavaScript. Built it as a node-based edito
