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🚀 Launching today: Postgres terminal written in Rust 🦀🐘 One terminal for all your Postgres databases. → Add any database in one command → Monitor health, queries, indexes & activity → Switch between databases instantly → Connect with pgbot for deeper diagnostics → Runs locally Free & open source... show more

alex shapalov

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51,465 views • 1 day ago •via X (Twitter)

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Just launched today 🤖🐘 Postgres intelligence for agents & apps. Free. Open source. Written in Go. Nobody wants to stare at PostgreSQL dashboards all day. So I built a bot that does it for you. → finds what’s wrong → explains what changed → tells you what to fix first Metrics are data. pgbot gives you answers pgbot can: +PostgreSQL health monitoring +Query performance analysis +Slow query and regression detection +Locks and blocking query insights +Table and index analytics +Vacuum and autovacuum visibility +Database growth tracking +AI root-cause analysis +AI-powered recommendations +Direct connection or agent-based private access

Just launched today 🤖🐘 Postgres intelligence for agents & apps. Free. Open source. Written in Go. Nobody wants to stare at PostgreSQL dashboards all day. So I built a bot that does it for you. → finds what’s wrong → explains what changed → tells you what to fix first Metrics are data. pgbot gives you answers pgbot can: +PostgreSQL health monitoring +Query performance analysis +Slow query and regression detection +Locks and blocking query insights +Table and index analytics +Vacuum and autovacuum visibility +Database growth tracking +AI root-cause analysis +AI-powered recommendations +Direct connection or agent-based private access

alex shapalov

97,655 views • 15 days ago

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Akshay 🚀

215,627 views • 7 months ago

YOU CAN NOW CONTROL ALL YOUR HERMES AGENTS FROM ONE INTERFACE. 🚨 Someone just built Hermes Workspace - the most complete GUI for Hermes Agent. Instead of jumping between tools, everything lives in one place. What's inside: > Chat, terminal, memory browser, and skills manager > Run unlimited agents with 1 orchestrator > Kanban board to track everything your agents are doing > 2,000+ skills to install without leaving the workspace > Works with any LLM provider Stop managing agents manually and start running them like a pro 👀 100% Free. Open Source.

YOU CAN NOW CONTROL ALL YOUR HERMES AGENTS FROM ONE INTERFACE. 🚨 Someone just built Hermes Workspace - the most complete GUI for Hermes Agent. Instead of jumping between tools, everything lives in one place. What's inside: > Chat, terminal, memory browser, and skills manager > Run unlimited agents with 1 orchestrator > Kanban board to track everything your agents are doing > 2,000+ skills to install without leaving the workspace > Works with any LLM provider Stop managing agents manually and start running them like a pro 👀 100% Free. Open Source.

Oliver Prompts

19,409 views • 1 month ago

Your Hermes agent just got a frontier-class brain for $0 🤯 Tencent HY 3.0 is free on OpenRouter until July 21, and it was built for exactly this: stable tool calls, 256K context, agentic workflows. One line in your config: openrouter/tencent/hy3:free - 78.0 SWE-Bench Verified - 71.7 Terminal-Bench 2.1 - Different reasoning modes Most free models running agents today are 20B-class. This one competes with GLM-5.1 for FREE.

Your Hermes agent just got a frontier-class brain for $0 🤯 Tencent HY 3.0 is free on OpenRouter until July 21, and it was built for exactly this: stable tool calls, 256K context, agentic workflows. One line in your config: openrouter/tencent/hy3:free - 78.0 SWE-Bench Verified - 71.7 Terminal-Bench 2.1 - Different reasoning modes Most free models running agents today are 20B-class. This one competes with GLM-5.1 for FREE.

Alvaro Cintas

15,431 views • 1 month ago

Goldman pays $27,000 per seat for a Bloomberg Terminal. I found 10 open source tools on GitHub that replicate almost all of it for free. Retail investors have never had this much firepower. Bookmark & Repost this one: 1. OpenBB Stocks, options, crypto, forex, and macro data in one research platform. Build your own dashboards, reports, and AI analysts on top of it. The OG of open source finance. 50K+ stars. 2. FinceptTerminal A full financial terminal: global market data, advanced charts, economic indicators, portfolio analysis, and AI research tools. Windows, Mac, and Linux. 3. Neuberg 516 drag-and-drop panels covering equities, bonds, commodities, currencies, credit, and macro. Even connects to Alpaca, Hyperliquid, and Polymarket so you can trade from the terminal itself. 4. Qlib (by Microsoft) An open source AI platform for quant investing. Train ML models, discover signals, backtest strategies, and build portfolios with the same workflow a quant desk uses. 5. FinRobot An AI equity research team on your laptop. Its agents read financial statements, build DCF valuations, debate bull vs bear cases, and generate full investment reports. 6. EdgarTools Turns the SEC database into something humans can actually use. Pull 10-Ks, 10-Qs, insider trades, executive pay, and hedge fund holdings going back to 1994. 7. LEAN (by QuantConnect) An institutional-grade engine for trading algorithms. Write strategies in Python or C#, backtest on decades of data, then connect to real brokers and go live. 8. FinanceToolkit 200+ financial ratios, valuation models, risk metrics, and economic indicators. Works on stocks, ETFs, options, currencies, commodities, and crypto from Python. 9. Ghostfolio A private wealth dashboard for stocks, ETFs, and crypto across all your accounts. Performance, allocation, diversification. Your data never leaves your machine. 10. OpenTerminalUI A self-hosted trading terminal: pro charts, screeners, options chains with live Greeks, portfolio optimization, backtesting, and an AI research agent. Runs entirely on your own hardware. Bloomberg spent 40 years building a $27,000/year moat. Open source is draining it one repo at a time. The software is free. Some live data feeds need your own API keys, but the barrier is now effort, not money. If you want the exact workflows we use to stack these tools with AI, join the AIBullss Discord:

Goldman pays $27,000 per seat for a Bloomberg Terminal. I found 10 open source tools on GitHub that replicate almost all of it for free. Retail investors have never had this much firepower. Bookmark & Repost this one: 1. OpenBB Stocks, options, crypto, forex, and macro data in one research platform. Build your own dashboards, reports, and AI analysts on top of it. The OG of open source finance. 50K+ stars. 2. FinceptTerminal A full financial terminal: global market data, advanced charts, economic indicators, portfolio analysis, and AI research tools. Windows, Mac, and Linux. 3. Neuberg 516 drag-and-drop panels covering equities, bonds, commodities, currencies, credit, and macro. Even connects to Alpaca, Hyperliquid, and Polymarket so you can trade from the terminal itself. 4. Qlib (by Microsoft) An open source AI platform for quant investing. Train ML models, discover signals, backtest strategies, and build portfolios with the same workflow a quant desk uses. 5. FinRobot An AI equity research team on your laptop. Its agents read financial statements, build DCF valuations, debate bull vs bear cases, and generate full investment reports. 6. EdgarTools Turns the SEC database into something humans can actually use. Pull 10-Ks, 10-Qs, insider trades, executive pay, and hedge fund holdings going back to 1994. 7. LEAN (by QuantConnect) An institutional-grade engine for trading algorithms. Write strategies in Python or C#, backtest on decades of data, then connect to real brokers and go live. 8. FinanceToolkit 200+ financial ratios, valuation models, risk metrics, and economic indicators. Works on stocks, ETFs, options, currencies, commodities, and crypto from Python. 9. Ghostfolio A private wealth dashboard for stocks, ETFs, and crypto across all your accounts. Performance, allocation, diversification. Your data never leaves your machine. 10. OpenTerminalUI A self-hosted trading terminal: pro charts, screeners, options chains with live Greeks, portfolio optimization, backtesting, and an AI research agent. Runs entirely on your own hardware. Bloomberg spent 40 years building a $27,000/year moat. Open source is draining it one repo at a time. The software is free. Some live data feeds need your own API keys, but the barrier is now effort, not money. If you want the exact workflows we use to stack these tools with AI, join the AIBullss Discord:

AI Bulls

20,605 views • 28 days ago

Okay... this is actually insane. OpenCodex feels like the open-source breakthrough I've been waiting for. The best part : You can plug multiple providers into the same OpenAI Codex harness and switch between models depending on the task. Running low on tokens? No problem. Use another provider. OpenRouter free model today? Plug it in. This completely changes how I think about AI coding workflows. And yes... it even works on mobile. OpenCodex might be one of the most useful open-source AI projects I've seen this year. OpenAI built an incredible harness. The open-source community just made it universal.

Okay... this is actually insane. OpenCodex feels like the open-source breakthrough I've been waiting for. The best part : You can plug multiple providers into the same OpenAI Codex harness and switch between models depending on the task. Running low on tokens? No problem. Use another provider. OpenRouter free model today? Plug it in. This completely changes how I think about AI coding workflows. And yes... it even works on mobile. OpenCodex might be one of the most useful open-source AI projects I've seen this year. OpenAI built an incredible harness. The open-source community just made it universal.

CHOI

42,894 views • 1 month ago

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 views • 6 months ago

Tip of the Day Overlay CVD and Open Interest directly onto your price chart. Pane-switch them onto price and ask one simple question: Are they moving with price or against it? Quick read: • Price ↑ + OI ↑ + CVD ↑ → new longs entering • Price ↑ + OI ↓ → short covering • Price ↓ + CVD selling but price holds → absorption Seeing them on the same pane as price makes these relationships instantly visible. Less guessing for clearer context. Try it out in the MMT Free Terminal.

Tip of the Day Overlay CVD and Open Interest directly onto your price chart. Pane-switch them onto price and ask one simple question: Are they moving with price or against it? Quick read: • Price ↑ + OI ↑ + CVD ↑ → new longs entering • Price ↑ + OI ↓ → short covering • Price ↓ + CVD selling but price holds → absorption Seeing them on the same pane as price makes these relationships instantly visible. Less guessing for clearer context. Try it out in the MMT Free Terminal.

MMT

32,154 views • 5 months ago

Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can plug in your OpenTelemetry data and see your agent’s process unfold: messages, tool calls, retries. Quotient AI automatically monitors, analyzes, and improves AI agents. Being able to review traces quickly is paramount for their research, and for their customers. “Dealing with agent traces was one of the biggest frustrations for our researchers and a huge time sink. All of that has gone away since adding AgentPrism, and we’re excited to bring that functionality to our users.” — Julia Neagu

Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can plug in your OpenTelemetry data and see your agent’s process unfold: messages, tool calls, retries. Quotient AI automatically monitors, analyzes, and improves AI agents. Being able to review traces quickly is paramount for their research, and for their customers. “Dealing with agent traces was one of the biggest frustrations for our researchers and a huge time sink. All of that has gone away since adding AgentPrism, and we’re excited to bring that functionality to our users.” — Julia Neagu

Evil Martians

101,372 views • 10 months ago

Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:

Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:

Rimsha Bhardwaj

89,515 views • 2 months ago

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,387 views • 2 months ago

China open-sourced a peanut-sized OCR that parses entire 100-page PDFs in one shot.. It's called Unlimited-OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. this one reads the whole thing in a single pass. → One-shot "long-horizon" parsing (32K context window) → Multilingual, out of the box → 93% on the standard parsing benchmark (+6 over baseline) → <0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50–$15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek-OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.

China open-sourced a peanut-sized OCR that parses entire 100-page PDFs in one shot.. It's called Unlimited-OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. this one reads the whole thing in a single pass. → One-shot "long-horizon" parsing (32K context window) → Multilingual, out of the box → 93% on the standard parsing benchmark (+6 over baseline) → <0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50–$15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek-OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.

Superman

1,100,999 views • 1 month ago

A guy in Vancouver built an entire operating system inside a Chrome tab. By himself. Over six years. His personal website is the OS. You open and a Windows-style desktop loads. File explorer. Start menu. Taskbar. You can drag in a zip and extract it. You can play DOOM. You can play Quake III Arena. You can boot Linux from an ISO. You can run Stable Diffusion locally for image generation. You can open a Python terminal. You can edit code in Monaco, the same engine that powers VS Code. All of it runs in your browser tab. Nothing installs. His name is Dustin Brett. Self-taught engineer. Father. Husband. 4,473 commits. All his. He had to swap the Windows icon for the π symbol because of legal pressure. The repo has 12,883 stars. MIT license. His hosting bill is one dollar a month. A single Cloudflare CDN does the rest. This is what the open web was built for. (Link in the comments)

A guy in Vancouver built an entire operating system inside a Chrome tab. By himself. Over six years. His personal website is the OS. You open and a Windows-style desktop loads. File explorer. Start menu. Taskbar. You can drag in a zip and extract it. You can play DOOM. You can play Quake III Arena. You can boot Linux from an ISO. You can run Stable Diffusion locally for image generation. You can open a Python terminal. You can edit code in Monaco, the same engine that powers VS Code. All of it runs in your browser tab. Nothing installs. His name is Dustin Brett. Self-taught engineer. Father. Husband. 4,473 commits. All his. He had to swap the Windows icon for the π symbol because of legal pressure. The repo has 12,883 stars. MIT license. His hosting bill is one dollar a month. A single Cloudflare CDN does the rest. This is what the open web was built for. (Link in the comments)

Nav Toor

72,629 views • 2 months ago

I replaced our Datadog bill with a single binary and cut infrastructure costs by 98% overnight. It’s called OpenObserve. Logs, metrics, traces, and frontend monitoring in one tool, self-hosted, and it’s built specifically to stop the bill that grows every time you add a host, a user, or a custom metric. → One binary, running in under 2 minutes. No cluster, no separate components for logs vs metrics vs traces → Built in Rust on the DataFusion query engine, so it stays fast even at petabyte scale → Uses Parquet columnar storage on S3-compatible object storage instead of a proprietary format, which is where the real cost savings come from → Query with SQL or PromQL instead of a vendor’s proprietary syntax, so your team isn’t learning a new query language just to read a dashboard → Full OpenTelemetry compatibility, no proprietary agents required to get your existing instrumentation talking to it → Community dashboard library on GitHub for Kubernetes, Docker, Postgres, AWS, and LLM observability, ready to drop in instead of building from scratch In OpenObserve’s own published benchmark, the same 16-service workload cost $174/day on Datadog and $3/day self-hosted, a 98% cut, before even touching per-host or per-seat fees. 18,000+ GitHub stars. Single binary. Self-hosted, no per-host or per-user tax.

I replaced our Datadog bill with a single binary and cut infrastructure costs by 98% overnight. It’s called OpenObserve. Logs, metrics, traces, and frontend monitoring in one tool, self-hosted, and it’s built specifically to stop the bill that grows every time you add a host, a user, or a custom metric. → One binary, running in under 2 minutes. No cluster, no separate components for logs vs metrics vs traces → Built in Rust on the DataFusion query engine, so it stays fast even at petabyte scale → Uses Parquet columnar storage on S3-compatible object storage instead of a proprietary format, which is where the real cost savings come from → Query with SQL or PromQL instead of a vendor’s proprietary syntax, so your team isn’t learning a new query language just to read a dashboard → Full OpenTelemetry compatibility, no proprietary agents required to get your existing instrumentation talking to it → Community dashboard library on GitHub for Kubernetes, Docker, Postgres, AWS, and LLM observability, ready to drop in instead of building from scratch In OpenObserve’s own published benchmark, the same 16-service workload cost $174/day on Datadog and $3/day self-hosted, a 98% cut, before even touching per-host or per-seat fees. 18,000+ GitHub stars. Single binary. Self-hosted, no per-host or per-user tax.

Harman

50,356 views • 1 month ago

Our @Grammarly AI agents are here! Today, we’re launching eight new AI agents designed for students and professionals. We created many of these agents with students in mind because they’re the first generation entering a job market where employers expect both subject expertise AND AI fluency. These agents help with everything from finding credible sources to predicting reader reactions. One agent we’ve gotten great feedback on is AI Grader (I wish I had this in school), which you can see in the video below. It looks at your assignment rubric and gives you suggestions like your professor would, and a grade prediction before you submit your work. And these agents are available in docs, our new AI-native writing surface! I’m deeply proud of this launch—docs is powered by Coda (Superhuman Docs) technology and is a great integration moment between Grammarly and Coda. This is just the beginning of Grammarly’s journey to offering agents that work everywhere people work and collaborate. I’ve been loving using these agents, and I’m excited for our customers to get access. Try them for yourself here and let me know what you think:

Our @Grammarly AI agents are here! Today, we’re launching eight new AI agents designed for students and professionals. We created many of these agents with students in mind because they’re the first generation entering a job market where employers expect both subject expertise AND AI fluency. These agents help with everything from finding credible sources to predicting reader reactions. One agent we’ve gotten great feedback on is AI Grader (I wish I had this in school), which you can see in the video below. It looks at your assignment rubric and gives you suggestions like your professor would, and a grade prediction before you submit your work. And these agents are available in docs, our new AI-native writing surface! I’m deeply proud of this launch—docs is powered by Coda (Superhuman Docs) technology and is a great integration moment between Grammarly and Coda. This is just the beginning of Grammarly’s journey to offering agents that work everywhere people work and collaborate. I’ve been loving using these agents, and I’m excited for our customers to get access. Try them for yourself here and let me know what you think:

Shishir

13,564 views • 1 year ago

traders, very soon I will make your life easier and help you multiply your capital a terminal with scalping mode is no longer a dream, but a reality that I have already taken on this terminal promises to break all boundaries and provide more opportunities for trading what I want to implement: > scalping mode: you will be able to trade lightning fast, open orders, and close them, just like in real scalp terminals > built-in indicators: everything in one place, no need to go anywhere > unique UI: To be honest, I'm tired of the boring UIs of other terminals, I'll make it really modern these are just the beginnings, but the most important part is done - I've started maybe you wanna try this? pre-alpha soon...

traders, very soon I will make your life easier and help you multiply your capital a terminal with scalping mode is no longer a dream, but a reality that I have already taken on this terminal promises to break all boundaries and provide more opportunities for trading what I want to implement: > scalping mode: you will be able to trade lightning fast, open orders, and close them, just like in real scalp terminals > built-in indicators: everything in one place, no need to go anywhere > unique UI: To be honest, I'm tired of the boring UIs of other terminals, I'll make it really modern these are just the beginnings, but the most important part is done - I've started maybe you wanna try this? pre-alpha soon...

morph

31,193 views • 6 months ago

The Amiko app is live on the Solana dApp store, and it’s our biggest release yet. Your Amiko twin doesn’t live at your desk anymore. Give your agent a task on the train. Run a compatibility profile when you meet someone. Do research, write code, build in the creative studio, whatever you need, from wherever you are. No laptop required. No waiting until you get home. Solanamobile users get two things Android and iOS won’t have at launch: Amiko token and crypto integration and on-device AI inference. Your twin runs locally on your phone if you want it to. Your behavioural profile, your data, your work, your twin. All on your hardware. AMIKO runs on OpenHermit, our own open-source agent runtime that we built in-house and released to the community. Most agent systems are designed for one agent talking to one person. OpenHermit is built for something different: agents talking to each other, coordinating across tasks, and collaborating with multiple humans simultaneously. That’s what makes features like compatibility profiling and multi-agent workflows actually work. We built it because nothing that existed was designed for this. Android and iOS are coming. Crypto integration and on-device AI are Solana Mobile exclusives. Most AI answers your questions. Amiko is an extension of you. Download →

AMIKO

124,860 views • 2 months ago

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

Miles Deutscher

81,972 views • 1 month ago