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Advice for AI engineers 💡 Small Audio LMs + Tool-calling LMs running locally are as fast as cloud speech APIs... ... but cost $0 per request and keep your data private. For example, LFM2.5-Audio-1.5B + LFM2-1.2B-Tool by Liquid AI ↓

Pau Labarta Bajo

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38,442 views • 6 months ago •via X (Twitter)

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Advice for AI engineers 💡 A small Visual Language Model fine-tuned on your custom dataset is as accurate as GPT-5... ... and costs 50 times less. For example, LFM2.5-VL-1.6B by Liquid AI running locally at full speed with llama.cpp ↓

Advice for AI engineers 💡 A small Visual Language Model fine-tuned on your custom dataset is as accurate as GPT-5... ... and costs 50 times less. For example, LFM2.5-VL-1.6B by Liquid AI running locally at full speed with llama.cpp ↓

Pau Labarta Bajo

80,190 views • 5 months ago

Advice for AI engineers 💡 A local AI assistant is just a while loop, an LLM and a set of tools. Here's a step-by-step lesson on how to build one from scratch with Liquid AI LFM2-24B-A2B running on llama.cpp. No cloud APIs. Enjoy ↓

Advice for AI engineers 💡 A local AI assistant is just a while loop, an LLM and a set of tools. Here's a step-by-step lesson on how to build one from scratch with Liquid AI LFM2-24B-A2B running on llama.cpp. No cloud APIs. Enjoy ↓

Pau Labarta Bajo

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Advice for AI engineers 💡 Browser control is possible with a Small Model, like LFM2-350M by Liquid AI . Here's a 60-minute deep dive on how to fine-tune with RL and OpenEnv by Hugging Face Enjoy ↓

Advice for AI engineers 💡 Browser control is possible with a Small Model, like LFM2-350M by Liquid AI . Here's a 60-minute deep dive on how to fine-tune with RL and OpenEnv by Hugging Face Enjoy ↓

Pau Labarta Bajo

16,169 views • 2 months ago

Introducing NexaSDK for Android (Beta) — run the latest AI models locally, 9× more energy-efficient and 2× faster, on Android devices, powered by the Qualcomm Hexagon NPU. This is the first SDK to support NPU, GPU and CPU, unlocking the full power of every Android device — for example, LFM2-1.2B achieves 85 t/s on NPU vs 37 t/s on CPU. With just 3 lines of code, you can run the latest state-of-the-art models across every task, for example: Multimodal (Vision, Audio, Text): OmniNeural-4B Embedding: EmbeddingGemma from Google ASR: Parakeet-v3 from NVIDIA OCR: PaddleOCR from Baidu Inc. Rerank: Jina-reranker from Jina AI LLM: LFM2-1.2B from And we continue to deliver Day-0 model support across our framework. Build on-device AI into your Android app today and enjoy no cloud API cost, full privacy, and offline availability. Check out our Quickstart guide and example app below to get started in minutes.

Introducing NexaSDK for Android (Beta) — run the latest AI models locally, 9× more energy-efficient and 2× faster, on Android devices, powered by the Qualcomm Hexagon NPU. This is the first SDK to support NPU, GPU and CPU, unlocking the full power of every Android device — for example, LFM2-1.2B achieves 85 t/s on NPU vs 37 t/s on CPU. With just 3 lines of code, you can run the latest state-of-the-art models across every task, for example: Multimodal (Vision, Audio, Text): OmniNeural-4B Embedding: EmbeddingGemma from Google ASR: Parakeet-v3 from NVIDIA OCR: PaddleOCR from Baidu Inc. Rerank: Jina-reranker from Jina AI LLM: LFM2-1.2B from And we continue to deliver Day-0 model support across our framework. Build on-device AI into your Android app today and enjoy no cloud API cost, full privacy, and offline availability. Check out our Quickstart guide and example app below to get started in minutes.

NEXA AI

26,091 views • 8 months ago

Small teams don't need an AI tool for every job title. They need one plan. Alibaba Cloud Token Plan consolidates text, image, video, and audio into a single credit pool, with access to Qwen and third-party models. Simplify your AI spend, starting at $4 for the first month.

Small teams don't need an AI tool for every job title. They need one plan. Alibaba Cloud Token Plan consolidates text, image, video, and audio into a single credit pool, with access to Qwen and third-party models. Simplify your AI spend, starting at $4 for the first month.

Alibaba Cloud

1,391,610 views • 2 days ago

Amazon Redshift RG, powered by AWS Graviton, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 at 30% lower cost per vCPU. Run SQL analytics across your data warehouse and data lake from a single engine — including on open formats such as Apache Iceberg and Apache Parquet. Built to meet the demands of today’s analytics and agentic AI workloads.

Amazon Redshift RG, powered by AWS Graviton, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 at 30% lower cost per vCPU. Run SQL analytics across your data warehouse and data lake from a single engine — including on open formats such as Apache Iceberg and Apache Parquet. Built to meet the demands of today’s analytics and agentic AI workloads.

Amazon Web Services

13,468 views • 2 months ago

VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

Julian Goldie SEO

59,167 views • 5 months ago

Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:

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Daniel San

65,228 views • 1 year ago

SECRET AI POWER TOOL. All modern Apple iOS phones have a a free powerful tool called “Recorder”. Not only is it a great way to take “notes”, it is a great way to get text to train your AI mode on your ideas, questions or statements. The built-in speech-to-text I use hourly. In fact this posting was made this way last night while I was driving. I also have been carrying a full off-Internet local voice memo device for decades for all of my audio interactions (WITH PERMISSION), not to use as evidence, but to build my high context AI, I call The Intelligence Amplifier. The Recorder app is used by me as a way to make quick snippets of my intentional audio. It is a “secret” to many, but I can say it is one. Very powerful free AI tool. I trained a small AI model on small engine repair by having a 71 year mechanic still working just explain in words everything he is doing over the course of 6 months, just his guiding voice. I hope to one source this. I also use this to send long prompts to Mr. Grok CEO ideas I have in our board of directors meetings. Give it a try. The dots in the upper right corner will have a Transcription option.

SECRET AI POWER TOOL. All modern Apple iOS phones have a a free powerful tool called “Recorder”. Not only is it a great way to take “notes”, it is a great way to get text to train your AI mode on your ideas, questions or statements. The built-in speech-to-text I use hourly. In fact this posting was made this way last night while I was driving. I also have been carrying a full off-Internet local voice memo device for decades for all of my audio interactions (WITH PERMISSION), not to use as evidence, but to build my high context AI, I call The Intelligence Amplifier. The Recorder app is used by me as a way to make quick snippets of my intentional audio. It is a “secret” to many, but I can say it is one. Very powerful free AI tool. I trained a small AI model on small engine repair by having a 71 year mechanic still working just explain in words everything he is doing over the course of 6 months, just his guiding voice. I hope to one source this. I also use this to send long prompts to Mr. Grok CEO ideas I have in our board of directors meetings. Give it a try. The dots in the upper right corner will have a Transcription option.

Brian Roemmele

27,193 views • 3 months ago

AI INFRASTRUCTURE/ENERGY DEMAND STOCKS TO KEEP ON WATCH FOR 2026 Ex-CEO $GOOGL Eric Schmidt on energy demand driven by AI: "We need energy and the numbers are profound. What we need from you is energy in all forms. People are planning 10 gigawatt data centers" $IREN: owns and develops vertically integrated data centers for hyperscalers $CIFR: large power footprint, 3.4 GW pipeline for AI workloads + HPC $WULF: high capacity, low cost data center expansion across Texas and New York $NBIS: AI compute and cloud services translating energy into revenue $CLSK: America’s Bitcoin Miner transitioning to AI infrastructure with a massive power footprint across 30+ data center sites $BITF: owns and operates power sites positioned for AI workloads, yet to sign with a hyperscaler but could come this year $GLXY: multi-gigawatt data center infrastructure at scale

AI INFRASTRUCTURE/ENERGY DEMAND STOCKS TO KEEP ON WATCH FOR 2026 Ex-CEO $GOOGL Eric Schmidt on energy demand driven by AI: "We need energy and the numbers are profound. What we need from you is energy in all forms. People are planning 10 gigawatt data centers" $IREN: owns and develops vertically integrated data centers for hyperscalers $CIFR: large power footprint, 3.4 GW pipeline for AI workloads + HPC $WULF: high capacity, low cost data center expansion across Texas and New York $NBIS: AI compute and cloud services translating energy into revenue $CLSK: America’s Bitcoin Miner transitioning to AI infrastructure with a massive power footprint across 30+ data center sites $BITF: owns and operates power sites positioned for AI workloads, yet to sign with a hyperscaler but could come this year $GLXY: multi-gigawatt data center infrastructure at scale

Small Cap Snipa

116,034 views • 6 months ago

Half an hour into the most recent Joe Rogan podcast, Joe and Matthew McConaughey imagined AI that could deeply understand you, learn from your private data, and give proactive support without sacrificing your privacy. AI that could know your favorite books, your personal information, even your thought patterns, and return insights to help you understand yourself better. They spoke of this like a vision for the future, but at LedgerAI, that future already exists. AuraVision is our private AI platform that keeps your data where it belongs, with you. •Your data is stored locally, not in the cloud. •You can ingest AuraVision with exquisite data about yourself or your company: your strategies, workflows, knowledge, and goals. • In return, AuraVision provides actionable advice and intelligence, turning your private information into secure, personal insights. Whether you are a doctor, lawyer, government body, a financial institution, or even Joe Rogan or Matthew McConaughey, AuraVision makes AI adoption real, practical, and secure, accessed via our native token, $LEDGER. The difference is simple: while others are still imagining, we’re already building. Train by day, AuraVision by night, all day! We’ve left a link to the episode in the replies, leave your thoughts!

Half an hour into the most recent Joe Rogan podcast, Joe and Matthew McConaughey imagined AI that could deeply understand you, learn from your private data, and give proactive support without sacrificing your privacy. AI that could know your favorite books, your personal information, even your thought patterns, and return insights to help you understand yourself better. They spoke of this like a vision for the future, but at LedgerAI, that future already exists. AuraVision is our private AI platform that keeps your data where it belongs, with you. •Your data is stored locally, not in the cloud. •You can ingest AuraVision with exquisite data about yourself or your company: your strategies, workflows, knowledge, and goals. • In return, AuraVision provides actionable advice and intelligence, turning your private information into secure, personal insights. Whether you are a doctor, lawyer, government body, a financial institution, or even Joe Rogan or Matthew McConaughey, AuraVision makes AI adoption real, practical, and secure, accessed via our native token, $LEDGER. The difference is simple: while others are still imagining, we’re already building. Train by day, AuraVision by night, all day! We’ve left a link to the episode in the replies, leave your thoughts!

AuraVision

1,547,864 views • 10 months ago

Today we're introducing Postman Fabric Gateway—an AI-native agent gateway. Here's why it matters: agents will consume APIs at several orders of magnitude beyond traditional apps. Traditional API Gateways are not designed for AI agents. While many legacy vendors have tried to support agentic workloads, Postman customers have been asking us about something that is AI-native and doesn’t have the baggage of pre-AI architecture. Postman Fabric has been designed from the ground-up for agents with support for MCPs, CLIs, as well as APIs - both AI and deterministic APIs. We are building this with a modular architecture as we are still in the early innings of AI and there are many new patterns that are likely to emerge. One thing is certain though, agents are driven by APIs, and you can’t have agent-ready APIs without a purpose-built gateway. Through Postman Fabric, customers will be able to: 1️⃣ Have a single plane to manage all MCP servers - public or private 2️⃣ Expose internal APIs as MCP tools 3️⃣ Centralize SaaS credentials 4️⃣ Govern agent traffic and run policies at a consumer, route, or service level 5️⃣ Deploy on cloud or in a hybrid model We are opening up early access soon - let me know if you are interested in the comments or a DM!

Today we're introducing Postman Fabric Gateway—an AI-native agent gateway. Here's why it matters: agents will consume APIs at several orders of magnitude beyond traditional apps. Traditional API Gateways are not designed for AI agents. While many legacy vendors have tried to support agentic workloads, Postman customers have been asking us about something that is AI-native and doesn’t have the baggage of pre-AI architecture. Postman Fabric has been designed from the ground-up for agents with support for MCPs, CLIs, as well as APIs - both AI and deterministic APIs. We are building this with a modular architecture as we are still in the early innings of AI and there are many new patterns that are likely to emerge. One thing is certain though, agents are driven by APIs, and you can’t have agent-ready APIs without a purpose-built gateway. Through Postman Fabric, customers will be able to: 1️⃣ Have a single plane to manage all MCP servers - public or private 2️⃣ Expose internal APIs as MCP tools 3️⃣ Centralize SaaS credentials 4️⃣ Govern agent traffic and run policies at a consumer, route, or service level 5️⃣ Deploy on cloud or in a hybrid model We are opening up early access soon - let me know if you are interested in the comments or a DM!

Abhinav Asthana

18,131 views • 2 months ago

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?

Alexey Grigorev

13,694 views • 4 months ago

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,424 views • 10 months ago

MITO AI is here. MITO AI is the tool we wish we had as filmmakers. It is not a prompt playground or a template generator. It is a control room for people who think in scenes, boards and timelines. You bring a story, references and intent. The tool helps you explore shots, workflows and versions faster than a traditional pipeline, without compromising taste. •⁠ ⁠Cinematic boards in minutes. •⁠ ⁠Keep your style consistent across images, scenes and videos. Designed by a studio that actually uses it every day. Join the early access list and help shape the tool:

MITO AI is here. MITO AI is the tool we wish we had as filmmakers. It is not a prompt playground or a template generator. It is a control room for people who think in scenes, boards and timelines. You bring a story, references and intent. The tool helps you explore shots, workflows and versions faster than a traditional pipeline, without compromising taste. •⁠ ⁠Cinematic boards in minutes. •⁠ ⁠Keep your style consistent across images, scenes and videos. Designed by a studio that actually uses it every day. Join the early access list and help shape the tool:

MITO AI

13,303 views • 7 months ago

🤖 We are entering the second revolution of Personal AI. AI agents aren’t just answering questions, they’re working for you 24/7 to help you stay ahead. Imagine a virtual team that continuously processes data, monitors systems, and executes tasks automatically even while you sleep. With AMD Agent Computers, your AI agents collaborate like a virtual team, maintaining context, sharing insights, and executing tasks as part of a coordinated agentic workflow, something that wasn’t possible before. Everything runs locally, keeping your data private and secure 🛡️ They think deeply, reasoning through problems, connecting insights, and making decisions that multiply your impact. Powered by Ryzen AI Max and Radeon AI PRO, your models, context, and tools live together, no token limits, fully local and private. 💡 Experience what a 24/7 AI team can do to multiply your impact beyond what you thought possible. 🔥 Join us April 30 in San Francisco at AMD Developer Day:

🤖 We are entering the second revolution of Personal AI. AI agents aren’t just answering questions, they’re working for you 24/7 to help you stay ahead. Imagine a virtual team that continuously processes data, monitors systems, and executes tasks automatically even while you sleep. With AMD Agent Computers, your AI agents collaborate like a virtual team, maintaining context, sharing insights, and executing tasks as part of a coordinated agentic workflow, something that wasn’t possible before. Everything runs locally, keeping your data private and secure 🛡️ They think deeply, reasoning through problems, connecting insights, and making decisions that multiply your impact. Powered by Ryzen AI Max and Radeon AI PRO, your models, context, and tools live together, no token limits, fully local and private. 💡 Experience what a 24/7 AI team can do to multiply your impact beyond what you thought possible. 🔥 Join us April 30 in San Francisco at AMD Developer Day:

Jack Huynh

24,609 views • 2 months ago