I just put together all my AI Agents posts in a single PDF.It covers:- Agent fundamentals- LLM vs RAG vs Agents- Agentic design patterns- Building Blocks of Agents- Building custom tools via MCP- 12 hands-on projects for AI EngineersDownload link in next tweet. https://t.co/LuQiyM5afF ...

Beware of gross margin in the early days. That's a mistake we've made a couple of times. You know, you have a lot of businesses that in the early days have really bad gross margin.All the LLM providers were very clear examples of that. I think if that's the only thing that's holding you up in most cases, I would totally ignore it. We never lose a deal or pass on the deal because of price in the early stage.So, we've been around for 30 years. We invested 11.5% billion. We've returned close to 30 and we still ...

7/Key insight:Don’t just naively compile an LLM into a circuit.Exploit structure:- Linear ops (MatMul, LayerNorm) → custom efficient constraints.- Nonlinear ops (GELU) → fused constraints to slash complexity.- Parallel-friendly layout to max out modern prover hardware. ...

Financial Data and Key Metrics Changes - Operating revenue increased by 13.5% year over year, reaching KRW 7,427.4 billion [6] - Operating profit rose by 105.4% year over year, amounting to KRW 1,014.8 billion, supported by balanced growth in the telco business and one-time gains from real estate sales [6] - Net income increased by 78.6% year over year to KRW 733.3 billion, driven by higher operating profit [6] - EBITDA grew by 36.3% year over year, reporting KRW 1,990.7 billion [6] - Operating expenses rose by 5.9% year over year, totaling KRW 6,412.6 billion [7] Business Line Data and Key Metrics Changes - Wireless revenue increased by 0.9% year on year, reporting KRW 1,781.7 billion, with 79.5% of total handset subscribers being 5G subscribers [8] - Fixed line broadband revenue grew by 2.1% year over year, reaching KRW 631.4 billion, driven by Giga Internet subscriber growth [9] - B2B service revenue posted a 4.5% year over year growth, supported by telecom and AI/IT services [11] - AIIT business revenues saw a significant increase of 13.8% year over year [11] - KT Cloud revenue grew by 23% year over year, driven by increased data center usage [12] Market Data and Key Metrics Changes - The company noted that the 5G penetration rate is above 80%, indicating a mature market [21] - The company observed no overheating of competition in the market following the launch of new flagship handsets, although future competition may arise with new iPhone releases [20] Company Strategy and Development Direction - The company is focused on transforming into an AICT company and enhancing corporate value through strategic initiatives [4][13] - A multi-model strategy is being implemented, including partnerships with global tech firms like Microsoft and Palantir to enhance competitiveness in AI services [17] - The company plans to invest KRW 1 trillion in information security over five years to improve customer safety in telecom services [5] Management Comments on Operating Environment and Future Outlook - Management expressed confidence in sustaining solid service revenue growth into the second half of the year, despite a significant one-off gain from real estate in Q2 [25] - Concerns were raised about potential increases in commissions and selling-related expenses, but these are linked to earnings performance [26] - The company is committed to maintaining a shareholder-friendly dividend policy, with a declared dividend of KRW 600 per share, a 20% increase year over year [4][27] Other Important Information - The company plans to complete a share buyback of KRW 250 billion and has outlined a future buyback plan totaling KRW 750 billion over the next three years [4][28] Q&A Session Summary Question: Future direction of AI business and impact of handset subsidy repeal - Management highlighted three main strategies for AI: partnerships with global tech firms, a multi-model strategy for AI service development, and leveraging AI capabilities for operational efficiency [17][19] - Regarding the M and P market, management noted that while competition may heat up with new handset launches, it is not expected to be long-lasting due to high 5G penetration and longer handset replacement cycles [20][21] Question: Outlook for the second half of the year and updates on the value plan - Management expressed optimism for continued strong performance in the second half, driven by solid service revenue and improved cost management [25] - The company confirmed its commitment to a shareholder-friendly dividend policy and plans for additional share buybacks as part of its value enhancement program [27][28]

Finally, the video shows prompting the LLM before and after fine-tuning.After fine-tuning, the model is able to generate the reasoning tokens in French before generating the final response in English.Check this 👇 https://t.co/v5eluK4xLK ...

All [Music] right, welcome everybody. Um, I'm Jeff Huber, the co-founder and CEO of Chroma, and I'm joined by Jason. We're going to do a two-parter here. We're really going to pack in the content.It's the last session of the day, and so we thought I'd give you a lot. Um everything in this presentation today is open source and code available. So we're also not selling you any tools.Um and so there'll be QR codes and stuff throughout to grab the code. So let's talk about how to look at your data. Um all of yo ...

[Music] Welcome everyone. I'm going to talk about practical tactics to build uh reliable AI applications and why nobody does it this way yet. Uh a little bit about myself or why you should trust me.Uh I have allowed 15 years as a startup co-founder and CTO. Uh I held executive positions for the last five years at uh several enterprises. uh but most importantly I spent last couple of years developing a lot of gen projects ranging from PC's to uh many production level uh solutions and helped some companies to ...

Your model works! It aces the evals! It even passes the vibe check! All that’s required is inference, right? Oops, you’ve just stepped into a minefield: -Not low-latency enough? Choppy experience. Users churn from your app. -Not cheap enough? You’re losing money on every query. -Not high enough output quality? Your system can’t be used for that application. A model and the inference system around it form a “token factory” associated with a Pareto frontier— a curve representing the best possible trade-offs b ...

Core Problem & Solution - The presentation introduces Exa, a search engine designed for AI, addressing the limitations of traditional search engines built for human users [5][23] - Exa aims to provide an API that delivers any information from the web, catering to the specific needs of AI systems [22][41] - Exa uses transformer-based embeddings to represent documents, capturing meaning and context beyond keywords [11][12] AI vs Human Search - Traditional search engines are optimized for humans who use simple queries and want a few relevant links, while AIs require complex queries, vast amounts of knowledge, and precise, controllable information [23][24] - AI agents need search engines that can handle multi-paragraph queries, search with extensive context, and provide comprehensive knowledge [31][32][33] - Exa offers features like adjustable result numbers (10, 100, 1000), date ranges, and domain-specific searches, giving AI systems full control [44] Market Positioning & Technology - Exa launched in November 2022 and gained traction for its ability to handle complex queries that traditional search engines struggle with [15] - The company recognized the need for AI-driven search after the emergence of ChatGPT, realizing that LLMs need external knowledge sources [17][18] - Exa combines neural and keyword search methods to provide comprehensive results, allowing agents to use different search types based on the query [47][48] Future Development - Exa is developing a "research endpoint" that uses multiple searches and LLM calls to generate detailed reports and structured outputs [51] - The company envisions a future where AI agents have full access to the world's information through a versatile search API [48] - Exa aims to handle a wider range of queries, including semantic and complex ones, turning the web into a controllable database for AI systems [38][39][40]

3️⃣ Tool calling- A human defines a set of tools the LLM can access to complete a task.- LLM decides when to use them and also the arguments for execution.Check this visual👇 https://t.co/HlxIDYRq6t ...