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🚨Introducing a new database: PGX 🐘 Postgres for AI agents. In Rust. One runtime. Thousands of Postgres databases. ⚡ Up to 100× less idle memory ⚡ 1,000 databases in just 659 MB RAM ⚡ Clone a 674 MB database in 85 ms ⚡ ~0.7 MB per idle database RUST.... show more

alex shapalov

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96,747 просмотров • 3 дней назад •via X (Twitter)

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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 Think htop for Postgres, but built for developers + AI agents. GitHub ↓

🚀 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 Think htop for Postgres, but built for developers + AI agents. GitHub ↓

alex shapalov

80,664 просмотров • 1 месяц назад

+ almost crossed 1,000 GitHub stars combined in just two weeks! ⭐️⭐️⭐️⭐️ Two open source tools for PostgreSQL: → pgbot Postgres diagnostics for humans and AI agents (Go) → pgterm: a fast Postgres terminal written in Rust A huge thank-you to everyone who tried them, starred them, and shared feedback 🐘 Next stop: 2,000 ⭐ :)

+ almost crossed 1,000 GitHub stars combined in just two weeks! ⭐️⭐️⭐️⭐️ Two open source tools for PostgreSQL: → pgbot Postgres diagnostics for humans and AI agents (Go) → pgterm: a fast Postgres terminal written in Rust A huge thank-you to everyone who tried them, starred them, and shared feedback 🐘 Next stop: 2,000 ⭐ :)

alex shapalov

41,236 просмотров • 1 месяц назад

Talking with someone the other day that estimated they had about 25,000 idle connections to Postgres. My actual response to them: "holy shit". They double checked, it was only about 12,000 Same day had a conversation with someone saying they didn't need pgbouncer because of activerecord's connection pooling. Let's dig into connection pooling in Postgres. Prior to Postgres 14 every connection to the database consumed memory, roughly 10MB, it may be slightly less but it still wasn't free. Even beyond Postgres 14 there is still various contention that happens when Postgres starts to use a connection. An application pooler maintains a set of connections and hands them out when needed on the application side. These are idle and real connections against the database that indeed do impact performance negatively. In contrast pgbouncer speaks the wire protocol, waits for the begin part of the transaction and then uses a connection. It more strictly manages how many idle ones it's having instead of per web server you're running. pgbouncer up until recently really needed to be run in transaction mode (which meant disabling prepared statements in your application framework). prepared_statement support in pgbouncer was added recently, and now you don't have to disable. Even when running with an older version of pgbouncer with prepared_statements disabled you'd still see a big performance gain. A quick check to know if you'd benefit from pgbouncer, run this query: SELECT count(*), state FROM pg_stat_activity GROUP BY 2; If you're idle account is high (yes this is dependent on your view, but to me if it's above 25-30 range, and especially if active is until half that) then you'd already start to benefit from pgbouncer. If it's at 10,000 then post haste get pgbouncer in place. Finally, you don't have to not use a framework pooler, they're fine, but don't think it replaces a native Postgres connection pooler.

Talking with someone the other day that estimated they had about 25,000 idle connections to Postgres. My actual response to them: "holy shit". They double checked, it was only about 12,000 Same day had a conversation with someone saying they didn't need pgbouncer because of activerecord's connection pooling. Let's dig into connection pooling in Postgres. Prior to Postgres 14 every connection to the database consumed memory, roughly 10MB, it may be slightly less but it still wasn't free. Even beyond Postgres 14 there is still various contention that happens when Postgres starts to use a connection. An application pooler maintains a set of connections and hands them out when needed on the application side. These are idle and real connections against the database that indeed do impact performance negatively. In contrast pgbouncer speaks the wire protocol, waits for the begin part of the transaction and then uses a connection. It more strictly manages how many idle ones it's having instead of per web server you're running. pgbouncer up until recently really needed to be run in transaction mode (which meant disabling prepared statements in your application framework). prepared_statement support in pgbouncer was added recently, and now you don't have to disable. Even when running with an older version of pgbouncer with prepared_statements disabled you'd still see a big performance gain. A quick check to know if you'd benefit from pgbouncer, run this query: SELECT count(*), state FROM pg_stat_activity GROUP BY 2; If you're idle account is high (yes this is dependent on your view, but to me if it's above 25-30 range, and especially if active is until half that) then you'd already start to benefit from pgbouncer. If it's at 10,000 then post haste get pgbouncer in place. Finally, you don't have to not use a framework pooler, they're fine, but don't think it replaces a native Postgres connection pooler.

Craig Kerstiens

12,411 просмотров • 2 лет назад

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

98,554 просмотров • 2 месяцев назад

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 🚀

215,785 просмотров • 9 месяцев назад

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

51,546 просмотров • 3 месяцев назад

Astra Nova x Alibaba Cloud 🤝 We’re fired up to officially announce our collaboration with Cloudician leveraging Alibaba Cloud, a global leader in AI and Cloud innovation — and the technical infrastructure backbone of our upcoming ecosystem product, TokenPlay.ai 🔥 Here’s what this power combo brings: ⚡ No-code game dev with Alibaba Cloud’s PAI platform ⚡ Hyper-intelligent gameplay powered by Qwen AI ⚡ AI-fueled engagement for Web3 creators This isn't another launch, this marks a bold step forward in building-next generation interactive worlds. is the biggest product we’ve ever built. Stay tuned — a new era is coming soon. 👀

Astra Nova x Alibaba Cloud 🤝 We’re fired up to officially announce our collaboration with Cloudician leveraging Alibaba Cloud, a global leader in AI and Cloud innovation — and the technical infrastructure backbone of our upcoming ecosystem product, TokenPlay.ai 🔥 Here’s what this power combo brings: ⚡ No-code game dev with Alibaba Cloud’s PAI platform ⚡ Hyper-intelligent gameplay powered by Qwen AI ⚡ AI-fueled engagement for Web3 creators This isn't another launch, this marks a bold step forward in building-next generation interactive worlds. is the biggest product we’ve ever built. Stay tuned — a new era is coming soon. 👀

Astra Nova

131,181 просмотров • 1 год назад

Despite being told no, I'm open-sourcing Chatpack. You can now deploy production-ready chat to any app with 30+ native chat features in a single command using npx chatpack@latest I wanted to build a simple chat layer where anyone could add 24/7 live messaging to their app without spending weeks rebuilding the same infrastructure from scratch. Connect your existing auth and database, and Chatpack handles everything underneath. So today, we're open-sourcing Chatpack. > 24/7 real-time messaging between your users > typing, presence, read receipts, permissions, and conversation history > works with your existing auth + database, so your users and data stay yours Run it as a CLI or library. Fully open source. Repo is MIT licensed. If I were starting an AI company today, I'd clone this, pick a market, and begin shipping with Claude Code. Truly excited to see what comes out of this.

Despite being told no, I'm open-sourcing Chatpack. You can now deploy production-ready chat to any app with 30+ native chat features in a single command using npx chatpack@latest I wanted to build a simple chat layer where anyone could add 24/7 live messaging to their app without spending weeks rebuilding the same infrastructure from scratch. Connect your existing auth and database, and Chatpack handles everything underneath. So today, we're open-sourcing Chatpack. > 24/7 real-time messaging between your users > typing, presence, read receipts, permissions, and conversation history > works with your existing auth + database, so your users and data stay yours Run it as a CLI or library. Fully open source. Repo is MIT licensed. If I were starting an AI company today, I'd clone this, pick a market, and begin shipping with Claude Code. Truly excited to see what comes out of this.

Daniel Ch

83,064 просмотров • 2 месяцев назад

Holy shit... Keygraph just built an AI that hacks your web app before hackers do. It's called Shannon and it's a fully autonomous AI pentester that finds REAL exploits, not just alerts. 96.15% success rate on the hint-free XBOW Benchmark. Your team ships code every day with Claude Code and Cursor. Your pentest? Once a year. That's 364 days of shipping vulnerabilities to production. Shannon closes that gap. What it actually does: → Autonomously hunts attack vectors in your source code → Uses a built-in browser to execute real exploits → Handles 2FA/TOTP logins with zero intervention → Delivers copy-paste Proof-of-Concepts (no false positives) → Runs Nmap, Subfinder, WhatWeb, Schemathesis under the hood Real results on OWASP Juice Shop in a single run: → 20+ high-impact vulnerabilities found → Complete auth bypass + full database exfiltration → Privilege escalation to admin via registration bypass → SSRF enabling internal network recon → Systemic IDOR across user data The architecture is what makes it work. 4 phases: Recon → Vuln Analysis → Exploitation → Reporting Specialized agents run in parallel for Injection, XSS, SSRF, and Broken Auth. Strict "No Exploit, No Report" policy kills false positives at the source. Covers the critical OWASP classes: - Injection - XSS - SSRF - Broken Authentication & Authorization One command. ~1 hour runtime. ~$50 per full pentest with Claude Sonnet. Every Claude (coder) deserves their Shannon. The Red Team to your vibe-coding Blue Team. 100% Opensource (AGPL-3.0). 10.6k stars already. Repo in reply ↓

Holy shit... Keygraph just built an AI that hacks your web app before hackers do. It's called Shannon and it's a fully autonomous AI pentester that finds REAL exploits, not just alerts. 96.15% success rate on the hint-free XBOW Benchmark. Your team ships code every day with Claude Code and Cursor. Your pentest? Once a year. That's 364 days of shipping vulnerabilities to production. Shannon closes that gap. What it actually does: → Autonomously hunts attack vectors in your source code → Uses a built-in browser to execute real exploits → Handles 2FA/TOTP logins with zero intervention → Delivers copy-paste Proof-of-Concepts (no false positives) → Runs Nmap, Subfinder, WhatWeb, Schemathesis under the hood Real results on OWASP Juice Shop in a single run: → 20+ high-impact vulnerabilities found → Complete auth bypass + full database exfiltration → Privilege escalation to admin via registration bypass → SSRF enabling internal network recon → Systemic IDOR across user data The architecture is what makes it work. 4 phases: Recon → Vuln Analysis → Exploitation → Reporting Specialized agents run in parallel for Injection, XSS, SSRF, and Broken Auth. Strict "No Exploit, No Report" policy kills false positives at the source. Covers the critical OWASP classes: - Injection - XSS - SSRF - Broken Authentication & Authorization One command. ~1 hour runtime. ~$50 per full pentest with Claude Sonnet. Every Claude (coder) deserves their Shannon. The Red Team to your vibe-coding Blue Team. 100% Opensource (AGPL-3.0). 10.6k stars already. Repo in reply ↓

Guri Singh

20,591 просмотров • 6 месяцев назад

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.

Hasan Toor

176,344 просмотров • 1 месяц назад

If you could describe ANY game in plain English… and watch AI build it for you — what would you make? 👀 That’s exactly what I tried with Gear Zero. No engine. No coding. No files. No installation. Just describe the game → AI plans it, codes it, creates the art, builds the levels → and you’re playing it in your browser. And it gets crazier: ⚡ First version in ~20 minutes 🤝 Build & shape games together 🧠 Deep Mode can work on one game for up to 10 hours 🎮 Play instantly on desktop or phone 🆓 Free to start This isn’t just AI generating a game. It’s AI helping you actually BUILD one. Describe your game. Watch it become real. Try Gear Zero 👇 Gear Zero

If you could describe ANY game in plain English… and watch AI build it for you — what would you make? 👀 That’s exactly what I tried with Gear Zero. No engine. No coding. No files. No installation. Just describe the game → AI plans it, codes it, creates the art, builds the levels → and you’re playing it in your browser. And it gets crazier: ⚡ First version in ~20 minutes 🤝 Build & shape games together 🧠 Deep Mode can work on one game for up to 10 hours 🎮 Play instantly on desktop or phone 🆓 Free to start This isn’t just AI generating a game. It’s AI helping you actually BUILD one. Describe your game. Watch it become real. Try Gear Zero 👇 Gear Zero

Succeeded Mind

22,880 просмотров • 1 месяц назад

🚨ICE agents are illegally stopping U.S. citizens and demanding they get out of their cars to “prove” their citizenship… in Shakopee, Minnesota. In the video, multiple unmarked agent vehicles have a man surrounded while they run his information through a federal database. A woman drives by, sees what’s happening, and asks the man his name. He answers her… and says he’s a U.S. citizen. And yet, he’s still being treated like he has to justify existing in his own community. And just to clarify… Agents cannot just stop someone, demand identification, and hold them while they “check” someone’s status without reasonable suspicion of a crime. Driving while being a POC is not reasonable suspicion. Federal agents do not get to turn neighborhoods into checkpoints because they feel like it. When armed agents surround someone, run them through a database, and force them to prove who they are without explaining why they’re being detained… that is a violation of their constitutional rights. Rights don’t hinge on whether an agent feels suspicious that day. When law enforcement believes the Constitution is optional, enforcement turns into intimidation. Stops turn into fishing expeditions. Detentions turn into threats. Databases turn into weapons. Power expands to the edge of whatever no one pushes back on. And once that line moves, it does not move back on its own. That’s what people should be paying attention to.

🚨ICE agents are illegally stopping U.S. citizens and demanding they get out of their cars to “prove” their citizenship… in Shakopee, Minnesota. In the video, multiple unmarked agent vehicles have a man surrounded while they run his information through a federal database. A woman drives by, sees what’s happening, and asks the man his name. He answers her… and says he’s a U.S. citizen. And yet, he’s still being treated like he has to justify existing in his own community. And just to clarify… Agents cannot just stop someone, demand identification, and hold them while they “check” someone’s status without reasonable suspicion of a crime. Driving while being a POC is not reasonable suspicion. Federal agents do not get to turn neighborhoods into checkpoints because they feel like it. When armed agents surround someone, run them through a database, and force them to prove who they are without explaining why they’re being detained… that is a violation of their constitutional rights. Rights don’t hinge on whether an agent feels suspicious that day. When law enforcement believes the Constitution is optional, enforcement turns into intimidation. Stops turn into fishing expeditions. Detentions turn into threats. Databases turn into weapons. Power expands to the edge of whatever no one pushes back on. And once that line moves, it does not move back on its own. That’s what people should be paying attention to.

Jesus Freakin Congress

156,099 просмотров • 8 месяцев назад

Introducing DeepConf: Deep Think with Confidence 🚀 First method to achieve 99.9% on AIME 2025 with open-source models! Using GPT-OSS-120B even without tools, we reached this almost-perfect accuracy while saving up to 85% generated tokens. It also delivers many strong advantages for parallel thinking: 🔥 Performance boost: ~10% accuracy across models & datasets ⚡ Ultra-efficient: Up to 85% fewer tokens generated 🔧 Plug & play: Works with ANY existing model - zero training needed (no hyperparameter tuning as well!) ⭐ Easy to deploy: Just ~50 lines of code in vLLM (see PR below) 📚 Paper: 🌐 Project: joint work with: Yichao Fu , xuewei_wang, Yuandong Tian (see details in the comments below)

Introducing DeepConf: Deep Think with Confidence 🚀 First method to achieve 99.9% on AIME 2025 with open-source models! Using GPT-OSS-120B even without tools, we reached this almost-perfect accuracy while saving up to 85% generated tokens. It also delivers many strong advantages for parallel thinking: 🔥 Performance boost: ~10% accuracy across models & datasets ⚡ Ultra-efficient: Up to 85% fewer tokens generated 🔧 Plug & play: Works with ANY existing model - zero training needed (no hyperparameter tuning as well!) ⭐ Easy to deploy: Just ~50 lines of code in vLLM (see PR below) 📚 Paper: 🌐 Project: joint work with: Yichao Fu , xuewei_wang, Yuandong Tian (see details in the comments below)

Jiawei Zhao

465,164 просмотров • 1 год назад

Holy shit...Claude Code just killed one of the most annoying terminal problems — flickering. 🤯 Anthropic quietly shipped NO_FLICKER mode and it changes how AI feels inside the terminal: • No more screen flashing • Smooth scrolling + stable viewport • Mouse support (yes… in terminal) • Constant memory/CPU as chats grow • Cleaner selections (no UI junk copied) This isn’t just a UI tweak. They basically virtualized the entire terminal viewport to control rendering themselves. Result? Terminal AI finally feels like a real app — fast, smooth, usable. ⚡ Turn it on: CLAUDE_CODE_NO_FLICKER=1 claude Small launch. Huge UX upgrade. Terminal-native AI is getting scary good.

Holy shit...Claude Code just killed one of the most annoying terminal problems — flickering. 🤯 Anthropic quietly shipped NO_FLICKER mode and it changes how AI feels inside the terminal: • No more screen flashing • Smooth scrolling + stable viewport • Mouse support (yes… in terminal) • Constant memory/CPU as chats grow • Cleaner selections (no UI junk copied) This isn’t just a UI tweak. They basically virtualized the entire terminal viewport to control rendering themselves. Result? Terminal AI finally feels like a real app — fast, smooth, usable. ⚡ Turn it on: CLAUDE_CODE_NO_FLICKER=1 claude Small launch. Huge UX upgrade. Terminal-native AI is getting scary good.

Suryansh Tiwari

32,145 просмотров • 6 месяцев назад

Vector Database by Hand ✍️ Vector databases are revolutionizing how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).

Vector Database by Hand ✍️ Vector databases are revolutionizing how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).

Tom Yeh

192,216 просмотров • 2 лет назад

Nobody is talking about this. Games inscribed on Bitcoin can now play multiplayer with each other. Think about what that means. Once a game is inscribed, hosting costs nothing. Forever. There's no reason larger studios won't eventually look at Bitcoin as a zero-cost distribution layer for their games and now those games can connect players directly. Introducing KENOBI. (Kryptographic Ephemeral Network for On-chain Bitcoin-Inscribed) gaming Shipped a major multiplayer upgrade to Beetlejuice Zombies (inscribed on Bitcoin). Used to be: host generates a code → sends to friend → friend generates answer → sends it back → host pastes it in. Every time. A pain. Now: host clicks "Host Room." Friend opens the same inscription, clicks "Browse Lobby," sees live rooms, picks one, clicks Join. Host accepts. Done. No central server. No database. No backend. The game lives in a Bitcoin inscription forever. Peers find each other and connect directly browser to browser. First of its kind on Ordinals. One-click multiplayer matchmaking. 100% on-chain. Permissionless. Extension-free. These aren't the servers you're looking for. 🖐️

Nobody is talking about this. Games inscribed on Bitcoin can now play multiplayer with each other. Think about what that means. Once a game is inscribed, hosting costs nothing. Forever. There's no reason larger studios won't eventually look at Bitcoin as a zero-cost distribution layer for their games and now those games can connect players directly. Introducing KENOBI. (Kryptographic Ephemeral Network for On-chain Bitcoin-Inscribed) gaming Shipped a major multiplayer upgrade to Beetlejuice Zombies (inscribed on Bitcoin). Used to be: host generates a code → sends to friend → friend generates answer → sends it back → host pastes it in. Every time. A pain. Now: host clicks "Host Room." Friend opens the same inscription, clicks "Browse Lobby," sees live rooms, picks one, clicks Join. Host accepts. Done. No central server. No database. No backend. The game lives in a Bitcoin inscription forever. Peers find each other and connect directly browser to browser. First of its kind on Ordinals. One-click multiplayer matchmaking. 100% on-chain. Permissionless. Extension-free. These aren't the servers you're looking for. 🖐️

口乃工-山卂𠘨丂卂丅口丂卄工 (🐸,🐸)

10,243 просмотров • 6 месяцев назад

someone just open-sourced their own neuro-sama. and it might be better than the original. it's called airi. a fully autonomous ai companion that talks to you in real time, plays minecraft and factorio with you, chats on discord and telegram, and has a live2d/vrm avatar body. runs entirely on your machine. → real-time voice conversations, speech recognition → animated avatar with auto-blink, eye tracking, idle animations → persistent memory across sessions → local inference via webgpu, no api calls needed supports 30+ llm providers, openai, claude, gemini, deepseek, ollama, groq, mistral, xai, local models. swap the brain with a config change. runs on native cuda and apple metal for real gpu acceleration. 17.5k stars. 101 contributors. 46 releases. 100% free. open source.

Oliver Prompts

289,874 просмотров • 2 месяцев назад

Building a business is hard as it is - so we’ve created this resource directory you can get for free. It has got all the resources (guides; templates; databases; discounts & free deals) you need as an entrepreneur to build your biz - curated & collected by Y Combinator alums who raised over $100M in venture capital; had exits; and built dozens of companies. It’s especially useful if you’re considering raising; whether angel or full-blown VC; there wouldn’t be a better time of the year. No comments needed - just chat with No Cap👽 via email or telegram: Why sharing this? This is just one of the many perks of No Cap’s free acceleration program called nc/acc - specifically targeting solopreneurs & lean AI-native teams.

Building a business is hard as it is - so we’ve created this resource directory you can get for free. It has got all the resources (guides; templates; databases; discounts & free deals) you need as an entrepreneur to build your biz - curated & collected by Y Combinator alums who raised over $100M in venture capital; had exits; and built dozens of companies. It’s especially useful if you’re considering raising; whether angel or full-blown VC; there wouldn’t be a better time of the year. No comments needed - just chat with No Cap👽 via email or telegram: Why sharing this? This is just one of the many perks of No Cap’s free acceleration program called nc/acc - specifically targeting solopreneurs & lean AI-native teams.

Alexander Nevedovsky

56,386 просмотров • 8 месяцев назад

Tired of trading electricity in a system built for institutions — not you? Carlos Aurelio Hernández felt the same. That’s why he went on a mission — to build a platform where energy professionals could finally trade on their own terms. Now, as CEO of Watt2Trade, he’s changing the game. Here’s how... ⚡🧵 Before building Watt2Trade, Carlos became one of the most respected voices in Mexico’s energy sector — with 35+ media appearances on platforms like Forbes, El Financiero, and Grupo Fórmula. He’s been at the table for every major energy discussion in the last decade. And more importantly — he’s built... ⚙️ HHGM – powering factories, refineries, and municipalities ⚡ SUJIO – cutting energy costs by up to 30% 💼 Volta Capital – backing the next wave of energy innovation Now Carlos is leading Watt2Trade — the first decentralized exchange where you can trade electricity directly. ✅ No brokers ✅ No approvals ✅ No red tape Just your wallet, your power — and a global marketplace open 24/7. It’s built by people who understand the energy market inside and out — and are finally putting it in your hands. This is the future of energy — and you’re invited to shape it. Your kilowatt is your currency. Your Watt is your Power.

Tired of trading electricity in a system built for institutions — not you? Carlos Aurelio Hernández felt the same. That’s why he went on a mission — to build a platform where energy professionals could finally trade on their own terms. Now, as CEO of Watt2Trade, he’s changing the game. Here’s how... ⚡🧵 Before building Watt2Trade, Carlos became one of the most respected voices in Mexico’s energy sector — with 35+ media appearances on platforms like Forbes, El Financiero, and Grupo Fórmula. He’s been at the table for every major energy discussion in the last decade. And more importantly — he’s built... ⚙️ HHGM – powering factories, refineries, and municipalities ⚡ SUJIO – cutting energy costs by up to 30% 💼 Volta Capital – backing the next wave of energy innovation Now Carlos is leading Watt2Trade — the first decentralized exchange where you can trade electricity directly. ✅ No brokers ✅ No approvals ✅ No red tape Just your wallet, your power — and a global marketplace open 24/7. It’s built by people who understand the energy market inside and out — and are finally putting it in your hands. This is the future of energy — and you’re invited to shape it. Your kilowatt is your currency. Your Watt is your Power.

Watt2Trade

28,792 просмотров • 1 год назад