Trending AI/ML Projects
FinRobot: An Open-Source AI Agent Platform for Financial Analysis using LLMs 🚀 🚀 🚀
Build, run and scale AI agents like API and microservices
Agent IDE that enables you to manage fleets of coding agents. It comes with an agentic orchestrator that plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews.
Use claude code, codex or pi for free from the terminal, IDE, or you phone like OpenClaw (voice supported)
🦄🦄🦄AI赋能股票分析：AI加持的股票分析/选股工具。股票行情获取，AI热点资讯分析，AI资金/财务分析，涨跌报警推送。支持A股，港股，美股。支持市场整体/个股情绪分析，AI辅助选股等。数据全部保留在本地。支持DeepSeek，OpenAI， Ollama，LMStudio，AnythingLLM，硅基流动，火山方舟，阿里云百炼等平台或模型。
AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨
Click to view documentation
GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.
Cataclysm - Dark Days Ahead. A turn-based survival game set in a post-apocalyptic world.
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents.
The GEP-powered self-evolving engine for AI agents. Auditable evolution with Genes, Capsules, and Events. | evomap.ai
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Clone any website with one command using AI coding agents
Click to view documentation
The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.
Click to view documentation
《明日方舟》小助手，全日常一键长草！| A one-click tool for the daily tasks of Arknights, supporting all clients.
The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.
Click to view documentation
Kimi Code CLI is your next CLI agent.
Click to view documentation
Anti-AI-slop design skill for Claude Code, Cursor, and Codex.
Click to view documentation
Voice-to-text dictation app with local (Nvidia Parakeet/Whisper) and cloud models (BYOK). Privacy-first and available cross-platform.
Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
Click to view documentation
A 15TB Collection of Physics Simulation Datasets
:hedgehog: PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP.
Click to view documentation
A custom launcher for Minecraft that allows you to easily manage multiple installations of Minecraft at once (Fork of MultiMC)
An open-source AI coding agent that lives in your terminal.
100+ AI Agent & RAG apps you can actually run — clone, customize, ship.
Click to view documentation
Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper live transcription, speaker diarization, and Ollama summarization built on Rust. 100% local processing. no cloud required. Meetily (Meetly Ai - https://meetily.ai) is the #1 Self-hosted, Open-source Ai meeting note taker for macOS & Windows. Understand How to write meeting minutes
Click to view documentation
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
JavaScript in-page GUI agent. Control web interfaces with natural language.
Extracted system prompts from Anthropic - Claude Fable 5, Opus 4.8, Claude Code, Claude Design. OpenAI - ChatGPT GPT-5.6, Codex GPT-5.6, GPT-5.5. Google - Gemini 3.5 Flash, 3.1 Pro, Antigravity. xAI - Grok, Cursor, Copilot, VS Code, Perplexity, and more. Updated regularly.
Click to view documentation
Terminal session manager for AI coding agents. One TUI for Claude, Gemini, OpenCode, Codex, and more.
🌐 The open-source Agentic browser; alternative to ChatGPT Atlas, Perplexity Comet, Dia.
World's first open-source, agentic video production system. 12 pipelines, 52 tools, 500+ agent skills. Turn your AI coding assistant into a full video production studio.
Click to view documentation
股票AI操盘手：从学习、模拟到实盘，一站式平台。包含股票知识、策略实例、大模型、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/
Never stop coding. Free MIT AI gateway: one endpoint, 268+ providers (50+ free), 500+ models — Claude, GPT, Gemini, Kimi K3, GLM, DeepSeek. Works with Claude Code, Codex, Cursor, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, multimodal, Desktop/PWA. Built by 500+ contributors.
Click to view documentation
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
AI agent toolkit: unified LLM API, agent loop, TUI, coding agent CLI
Click to view documentation
A collection of agent skills for CAD, robotics and hardware design
Repo for the Complete Agentic AI Engineering Course
Use Garry Tan's exact Claude Code setup: 23 opinionated tools that serve as CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
ggml speech-to-text inference for 16+ model families
Click to view documentation
Blazing fast, instant realtime GraphQL APIs on all your data with fine grained access control, also trigger webhooks on database events.
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Hyprland is an independent, highly customizable, dynamic tiling Wayland compositor that doesn't sacrifice on its looks.
OfficeCLI is the first and best Office suite purpose-built for AI agents to read, edit, and automate Word, Excel, and PowerPoint files. Free, open-source, single binary, no Office installation required.
Click to view documentation
AI agent to evaluate and score resumes.
Click to view documentation
Collection of publicly available IPTV channels from all over the world
Click to view documentation
The open-source AI voice studio. Clone, dictate, create.
Click to view documentation
A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations
Click to view documentation
基于 Claude Code 的长篇网文辅助创作系统，解决 AI 写作中的「遗忘」和「幻觉」问题，支持 200 万字量级 连载创作。
Skills for Real Engineers. Straight from my .agents directory.
Click to view documentation
holehe allows you to check if the mail is used on different sites like twitter, instagram and will retrieve information on sites with the forgotten password function.
Free, open-source web app for learning about ontologies and Microsoft Fabric IQ. Explore a catalogue of pre-built ontologies, design your own visually, export as RDF/XML, and share interactive diagrams. Zero backend, fully static.
Click to view documentation
A fork of Windows Terminal with native agent integration, right in your command line.
Very low latency speech to text, intent recognition, and text to speech, for building voice agents and interfaces
Click to view documentation
Reliable model swapping for any local OpenAI/Anthropic compatible server - llama.cpp, vllm, etc
All course materials for the Zero to Mastery Machine Learning and Data Science course.
A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.
Click to view documentation
LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
Every API has a secret identity. This finds it, absorbs every feature from every competing tool, then builds the GOAT CLI — designed for AI agents first, with SQLite sync, offline search, and compound insight commands.
agent multiplexer that lives in your terminal.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Lightweight coding agent that runs in your terminal
Click to view documentation
A coding agent for open models like Kimi K3
Native web workspace for Hermes Agent — chat, terminal, memory, skills, inspector.
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Learn it. Build it. Ship it for others.
Click to view documentation
Open-source AI job search: scan job portals, score listings A-F, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Gemini, Codex, OpenCode…)
SimpleX - the first messaging network operating without user identifiers of any kind - 100% private by design! iOS, Android and desktop apps 📱!
Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop and mobile.
Click to view documentation
Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
Click to view documentation
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
Click to view documentation
Self-hosted realtime soundscape analyser for birds, bats and other wildlife. Multi-model local AI inference, runs 24/7 on a Raspberry Pi.
AI-assisted TradingView chart analysis — connect Claude Code to your TradingView Desktop for personal workflow automation
Any agent Skill: generate beautiful architecture diagrams with dark/light theme toggle and PNG/JPEG/WebP/SVG export
Open-source AI penetration testing tool to find and fix your app’s vulnerabilities.
Click to view documentation
Deepsec is a security harness for finding vulnerabilities in your codebase powered by coding agents
This is MCP server for Claude that gives it terminal control, file system search and diff file editing capabilities
🎬 seedance2接入 开源本地 AI 短剧 & 漫剧生成工具 —— 从故事到成片一站式完成，数据不出本机，短剧工作流管理平台，高灵活度，AI真人剧，AI漫剧本地搞定。 Open-source local AI short drama maker: story → storyboard → video, fully offline, your data stays yours. 纳米流水线
Latest AI Research Articles
Summary
Imagine your AI assistant just produced 200 lines of code. Legally, you may not own a single line of...
Core Contributions
- Imagine your AI assistant just produced 200 lines of code.
- Legally, you may not own a single line of....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
Third entry in the DEV x Sentry Bug Smash. Entry 1 was a crash with a confusing message. Entry 2 was...
Core Contributions
- Third entry in the DEV x Sentry Bug Smash.
- Entry 1 was a crash with a confusing message.
- Entry 2 was....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
Summary
What happened when I tried to deploy a CrewAI agent to AWS Bedrock AgentCore. Every error was a 200 OK. Every fix took hours to find. Here's the full debugging trail.
Core Contributions
- What happened when I tried to deploy a CrewAI agent to AWS Bedrock AgentCore.
- Every error was a 200 OK.
- Every fix took hours to find.
Summary
Introduction At recent technology conferences, one topic has caught my attention: every...
Core Contributions
- Introduction At recent technology conferences, one topic has caught my attention: every....
Summary
Scope note (read first): this describes a system I built and operate to develop an unannounced game....
Core Contributions
- Scope note (read first): this describes a system I built and operate to develop an unannounced game.....
Summary
When I first wrote about NutriAgent in November 2025, it was a full application. It had a Python...
Core Contributions
- When I first wrote about NutriAgent in November 2025, it was a full application.
- It had a Python....
Summary
In the last entry I got Gemma-4's 128-expert MoE running on an inf2.24xlarge and signed off with...
Core Contributions
- In the last entry I got Gemma-4's 128-expert MoE running on an inf2.24xlarge and signed off with....
Summary
If you use Claude, Cursor, or any MCP-compatible AI assistant, you've probably noticed how useful it...
Core Contributions
- If you use Claude, Cursor, or any MCP-compatible AI assistant, you've probably noticed how useful it....
Summary
Hi HN, I’m Alex Southmayd, the founder of Bloomy (<a href="https://bloomylearning.com">https://bloomylearning.com</a>) – an AI-powered mastery-learning platform for K-12 students. Bloomy provides students with an AI tutor alongside adaptive curriculum (right now Math, English Language Arts, and Writing).<p>How it works: we diagnose students’ skill gaps, place them on personalized learning paths, and give them standards-aligned lessons and a Socratic AI tutor that scaffolds their learning without just giving away the answer.<p>The goal is to solve the Bloom 2-sigma problem (<a href="https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem" rel="nofollow">https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem</a>) with AI.<p>Short launch video: <a href="https://tinyurl.com/bloomylearning" rel="nofollow">https://tinyurl.com/bloomylearning</a><p>Longer product demo: <a href="https://youtu.be/XHvoKt6qMeo" rel="nofollow">https://youtu.be/XHvoKt6qMeo</a><p>Families access for Bloomy: <a href="https://bloomylearning.com/families">https://bloomylearning.com/families</a><p>I started as a teacher. I taught 7th-grade English and writing with Teach For America, and every day I struggled to deliver differentiated instruction to 30 students with 30 different sets of needs. Some students needed remediation, some needed acceleration, and many needed a tutor sitting next to them helping them reason through the next step. Benjamin Bloom’s two-sigma result—that one-on-one tutoring can produce much better outcomes than conventional classroom instruction—always felt intuitively true to me. The hard part was making that kind of attention affordable and available to every child.<p>Then AI changed the cost curve. When I saw schools such as Alpha organize academics around mastery rather than seat time, the model clicked. If you’ve heard of Alpha School, that is directionally the kind of learning model that inspired us. But I kept thinking about the families and schools that already exist: homeschool families, microschools, hybrid schools, and regular classrooms where most children are today.<p>Most students and teachers see learning gaps at the wrong resolution. They get a grade, percentile, benchmark score, or broad standard—not “this is the next skill this student should learn.” Existing personalized-learning products often feel like digital worksheets: they provide plenty of practice, but not much diagnosis or teaching. Very few have AI tutors providing the core instruction. Bloomy starts with a diagnostic—we integrate with third-party assessments and provide our own—and creates a learning path for each student. Students work one skill at a time, receive a short lesson, practice at an adaptive difficulty, and only move forward after demonstrating at least 90% mastery. The learning path updates as the student works, based on their performance and our knowledge graph of skill prerequisites (built in collaboration with Learning Commons / Chan Zuckerberg Initiative).<p>Each skill has three stages. Base Camp teaches the concept with worked examples. Climb provides guided practice and Socratic support. Summit is an independent ten-question mastery assessment with no hints or AI assistance. Students need to achieve 90% on the Summit to advance. If they struggle too much, they’ll be routed to a different skill better suited for their level.<p>BloomyBot is not a blank chat window but rather a live, interactive, and observant digital tutor. During practice, it receives the active passage or problem, the question, the student’s attempt, an authored explanation, and relevant misconception context. It follows a scaffolded tutoring ladder: first asking what the student tried, then pointing toward the concept, suggesting a strategy, working through one step together, and only providing heavier scaffolding after the student has struggled, adapting to and learning from the student along the way. Students can interrupt it, and we’ve begun to roll out multilingual support for Spanish, French, and a few other more niche languages that customers have asked for.<p>We currently use a variety of Anthropic and OpenAI models for BloomyBot. The tutor is restricted to the current lesson, redirects unrelated questions, limits conversation length, and is unavailable during mastery assessments. The language model does not choose the curriculum or decide whether a student has mastered a skill.<p>That separation is important. A conventionally “helpful” AI response can be a bad tutoring response: if it gives away the answer, the student completes the task but may not learn anything. Our goal is not to build a homework-answering chatbot. It is to put AI inside a structured loop of diagnosis, instruction, practice, feedback, and independent mastery.<p>LLMs can still be wrong, and we do not claim our constraints eliminate that. We reduce the surface area by grounding BloomyBot in authored lesson content, keeping it on topic, logging conversations, and removing it from assessments. Teachers and parents can review tutoring activity, students can report problems, and safety signals trigger human alerts and a backup audit. We also do not see Bloomy as a replacement for teachers, parents, or human tutors. A good human tutor is better. The narrower question we are testing is whether, during a bounded learning session a student would already be doing, a context-aware tutor can provide better help than static “correct/incorrect” feedback. Longer term, the question becomes more whether a student would perform better with one-on-one AI tutoring (at least in certain aspects of the curriculum) than with many-to-one instruction in a medium- or large-sized classroom.<p>Bloomy is now being used across several settings: traditional districts, charter schools, hybrid schools, microschools, homeschools, and families looking for additional academic support. In an early pilot at a charter school in Massachusetts serving ~150 students in grades 6 through 8, students averaged roughly 1.8 times the expected winter-to-spring NWEA MAP growth. This was an observational pilot, not a randomized study, so we treat it as an encouraging signal rather than proof that Bloomy caused the difference.<p>Parents and teachers can see what a student has mastered, what is in progress, and where support may be needed. We have found that adults generally do not want another generic score; they want to know which small number of skills deserve attention this week.<p>Bloomy makes money through family subscriptions and school licensing. ELA costs $39/month or $279/year per learner, and Writing Studio costs $19/month or $139/year. Math is scheduled to launch July 31 at the same price as ELA. Schools and microschools pay per student, with pricing varying by subject coverage, enrollment, rostering, and implementation needs.<p>Because children use Bloomy, we collect learning responses, progress data, and tutoring conversations. We do not sell personal information, use child data for behavioral advertising, or permit model providers to train general-purpose models on identifiable child data sent by Bloomy. We have Zero Data Retention agreements with both Anthropic and OpenAI. Parents and schools can request access, export, correction, or deletion under the applicable account or school agreement.<p>More background on me: after Teach For America, I taught and designed GMAT and GRE curriculum for Manhattan Prep / Kaplan, led the driver acquisition team for Lyft’s New England markets, completed an MBA at Stanford, and led AI transformation projects at McKinsey (so when models finally became good enough this past January to achieve the kinds of things I am pursuing with Bloomy, I was in the right place at the right time to begin building). Bloomy brings together the different parts of my career that I care most about: educational outcomes, learning design, building products, and getting useful technology into people’s hands.<p>I’d especially value feedback from parents, teachers, engineers working on child-facing AI, and people who have built tutoring, assessment, or adaptive-learning systems. Does the separation between guided AI help and independent mastery make sense? Where do you see the greatest potential with AI in education? Where are our safeguards insufficient? What evidence or product behavior would you need to trust something like this with a student?<p>Certainly there are many dangers and pitfalls we must beware of, too, but I believe we can really move the needle in K-12 (for the first time in a long time) if we use AI responsibly and intelligently.
Core Contributions
- Hi HN, I’m Alex Southmayd, the founder of Bloomy (<a href="https://bloomylearning.com">https://bloomylearning.com</a>) – an AI-powered mastery-learning platform for K-12 students.
- Bloomy provides students with an AI tutor alongside adaptive curriculum (right now Math, English Language Arts, and Writing).<p>How it works: we diagnose students’ skill gaps, place them on personalized learning paths, and give them standards-aligned lessons and a Socratic AI tutor that scaffolds their learning without just giving away the answer.<p>The goal is to solve the Bloom 2-sigma problem (<a href="https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem" rel="nofollow">https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem</a>) with AI.<p>Short launch video: <a href="https://tinyurl.com/bloomylearning" rel="nofollow">https://tinyurl.com/bloomylearning</a><p>Longer product demo: <a href="https://youtu.be/XHvoKt6qMeo" rel="nofollow">https://youtu.be/XHvoKt6qMeo</a><p>Families access for Bloomy: <a href="https://bloomylearning.com/families">https://bloomylearning.com/families</a><p>I started as a teacher.
- I taught 7th-grade English and writing with Teach For America, and every day I struggled to deliver differentiated instruction to 30 students with 30 different sets of needs.
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
There are plenty of good SQL clients. I built another one. For a long time the honest answer to...
Core Contributions
- There are plenty of good SQL clients.
- I built another one.
- For a long time the honest answer to....
Summary
Capture the ringleader first. The rest will scatter on their own. — The 36 Stratagems, Capture the...
Core Contributions
- Capture the ringleader first.
- The rest will scatter on their own.
- — The 36 Stratagems, Capture the....
Summary
Preliminary In the past, I have tried a few things to get local video and image generation...
Core Contributions
- Preliminary In the past, I have tried a few things to get local video and image generation....
Summary
A bug showed up in my personal project last month. Nothing dramatic - a value wasn't updating the way...
Core Contributions
- A bug showed up in my personal project last month.
- Nothing dramatic - a value wasn't updating the way....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
I build small LLM agents. Not the impressive kind you see in demos, just practical little things that...
Core Contributions
- I build small LLM agents.
- Not the impressive kind you see in demos, just practical little things that....
Summary
I build a lot of side projects with AI coding tools. Mostly Claude Code. A few months ago I noticed...
Core Contributions
- I build a lot of side projects with AI coding tools.
- Mostly Claude Code.
- A few months ago I noticed....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
The MoE was the hard one: a dual-path FFN, a sparse expert loop that won't trace, and a bug where the device output was empty while the CPU reference was perfect and every unit test passed.
Core Contributions
- The MoE was the hard one: a dual-path FFN, a sparse expert loop that won't trace, and a bug where the device output was empty while the CPU reference was perfect and every unit test passed..
Summary
This is my second entry for the DEV x Sentry Bug Smash challenge. Entry #1 was a crash with a...
Core Contributions
- This is my second entry for the DEV x Sentry Bug Smash challenge.
- Entry #1 was a crash with a....
Summary
I built a pytest suite for a small AI agent - not a model that answers once, but one that plans,...
Core Contributions
- I built a pytest suite for a small AI agent - not a model that answers once, but one that plans,....
Summary
Chrome ships a real LLM inside the browser now — Gemini Nano, exposed through a handful of built-in...
Core Contributions
- Chrome ships a real LLM inside the browser now — Gemini Nano, exposed through a handful of built-in....
Summary
Toss out a brick to lure a jade gem. — The 36 Stratagems, Throw Out a Brick to Get a...
Core Contributions
- Toss out a brick to lure a jade gem.
- — The 36 Stratagems, Throw Out a Brick to Get a....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry. ...
Core Contributions
- This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry.
- ....
Summary
I made ClickHouse and Postgres Fight to Find Out 🥊 One Mac Mini. Two databases. 100 million rows of...
Core Contributions
- I made ClickHouse and Postgres Fight to Find Out 🥊 One Mac Mini.
- Two databases.
- 100 million rows of....
Summary
I've been hearing the word "harness" thrown around a lot lately. I assumed it just meant "the IDE" or...
Core Contributions
- I've been hearing the word "harness" thrown around a lot lately.
- I assumed it just meant "the IDE" or....
Summary
This article is my submission for the Agents of SigNoz Hackathon: Blog Track, where participants...
Core Contributions
- This article is my submission for the Agents of SigNoz Hackathon: Blog Track, where participants....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
(yep, kinda clickbait, just for the funsies 😊) At the beginning of the year, I relaunched my...
Core Contributions
- (yep, kinda clickbait, just for the funsies 😊) At the beginning of the year, I relaunched my....
Summary
When the enemy occupies favorable terrain, don't attack head-on. Let them think they're safe, let...
Core Contributions
- When the enemy occupies favorable terrain, don't attack head-on.
- Let them think they're safe, let....
Summary
Porting Gemma-4 31B (dense) to AWS Inferentia2: the tensor-parallel recipe that worked at 12B collapses at 31B, NxD ModelBuilder saves it — and then a passing validation lies to your face.
Core Contributions
- Porting Gemma-4 31B (dense) to AWS Inferentia2: the tensor-parallel recipe that worked at 12B collapses at 31B, NxD ModelBuilder saves it — and then a passing validation lies to your face..
Summary
My laptop was sitting idle with the fan at full tilt. Nothing was running that I knew of. The culprit...
Core Contributions
- My laptop was sitting idle with the fan at full tilt.
- Nothing was running that I knew of.
- The culprit....
Summary
In the previous post, we built a RAG system from scratch. Sixty lines of Python. Six onboarding...
Core Contributions
- In the previous post, we built a RAG system from scratch.
- Sixty lines of Python.
- Six onboarding....
Summary
Summary
There are a lot of shallow "10x with AI" threads out there. This isn't one of them. This is the...
Core Contributions
- There are a lot of shallow "10x with AI" threads out there.
- This isn't one of them.
- This is the....
Summary
When was the last time when you were in the state of flow? Where hours passed by unnoticed as you...
Core Contributions
- When was the last time when you were in the state of flow? Where hours passed by unnoticed as you....