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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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38,504 次观看 • 8 个月前 •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 ↓

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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 ↓

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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 ↓

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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.

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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.

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Mike

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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? 👇

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🚨 THIS IS INSANE A developer has released a free AI voice studio capable of cloning almost any voice in just a few seconds. It's called Voicebox. And it has already surpassed 50,500 stars on GitHub. Here's how it works: 1.- Upload a short audio sample and clone a voice in seconds. 2.- Generate speech in 23 different languages using multiple TTS engines. 3.- Run everything locally for greater privacy, and you can integrate it into your own applications. It also includes voice dictation, support for AI agents, and MCP integration. All in a single open-source tool. Save this tool. I've left the link in the next comment 👇

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Daniel San

65,228 次观看 • 1 年前

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.

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Small Cap Snipa

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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

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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!

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Dylan explains why the next wave of private equity looks nothing like the last one. "If we're looking at these companies that are AI rollups, let's take an existing company and completely destroy its cost structure. Nuke its cost structure by just making it efficient with AI." "Private equity companies buy a company, and right now they just squeeze the rag, discard it, and make the American populace screwed." "What new people started to do is the AI private equity strategy. Instead of trying to wring it dry, they're modernizing all the systems. Oh, you use Excel for your databases? Okay, let's move to standard cloud. Spend a lot of money up front." "AI makes that front load much more severe. You spike up on spend a lot for the one time, and then you spike down a lot and your cost efficiency is way better. AI cold calling, AI invoicing, AI accounting, none of it is really at critical mass. But we're so close."

Dylan explains why the next wave of private equity looks nothing like the last one. "If we're looking at these companies that are AI rollups, let's take an existing company and completely destroy its cost structure. Nuke its cost structure by just making it efficient with AI." "Private equity companies buy a company, and right now they just squeeze the rag, discard it, and make the American populace screwed." "What new people started to do is the AI private equity strategy. Instead of trying to wring it dry, they're modernizing all the systems. Oh, you use Excel for your databases? Okay, let's move to standard cloud. Spend a lot of money up front." "AI makes that front load much more severe. You spike up on spend a lot for the one time, and then you spike down a lot and your cost efficiency is way better. AI cold calling, AI invoicing, AI accounting, none of it is really at critical mass. But we're so close."

SemiAnalysis

72,852 次观看 • 1 个月前

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 次观看 • 6 个月前