Tessl

We're hiring a Developer Growth Marketer at Tessl! Join our team to be a part of the next big AI dev motion - AI Native Development. Please reach out if you have any questions! If you're thinking about applying, but not quite sure, just apply! https://lnkd.in/ejgUSivQ

🔙 𝐓𝐡𝐫𝐨𝐰𝐛𝐚𝐜𝐤 𝐭𝐨 𝐄𝐩𝐢𝐬𝐨𝐝𝐞 1 – 𝐓𝐡𝐞 𝐒𝐭𝐚𝐫𝐭 𝐨𝐟 𝐀𝐈 𝐍𝐚𝐭𝐢𝐯𝐞 𝐃𝐞𝐯 𝐏𝐨𝐝𝐜𝐚𝐬𝐭! 🎙️🚀 Seven months ago, we launched the AI Native Dev Podcast with our very first episode, diving into 𝐀𝐈 𝐍𝐚𝐭𝐢𝐯𝐞 𝐯𝐬. 𝐀𝐈 𝐀𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭, the evolving role of developers, and how AI is revolutionizing software just like the cloud once did. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐫𝐨𝐦 𝐄𝐩𝐢𝐬𝐨𝐝𝐞 1: 🛠️ AI-native development isn’t just about adding AI tools—it’s about rethinking how we build software from the ground up. ⚡ AI-assisted development (like Copilot) enhances traditional methods, but AI-native is about fully integrating AI into the development process. 🧑💻 The role of developers is evolving—soon, coding may be more about solving problems with high-level concepts rather than writing every line of code manually. 🌍 Tessl’s mission is to shape this transformation by creating a platform designed for AI-native development. Looking back, it’s incredible to see how much the conversation has evolved! If you’ve been with us since the beginning—thank you! If you’re new, now’s the perfect time to catch up.

DeepSeek R1: The Future of Open AI? The AI world is buzzing about DeepSeek AI R1, an open weights model that’s shaking up the space with its cost-efficient training ($5.6M vs. the estimated $100M+ for OpenAI’s GPT-4) and innovative approach to AI development. But here’s the big question: 💡 𝐇𝐨𝐰 𝐨𝐩𝐞𝐧 𝐢𝐬 𝐚𝐧 𝐨𝐩𝐞𝐧 𝐰𝐞𝐢𝐠𝐡𝐭𝐬 𝐦𝐨𝐝𝐞𝐥? In a live-streamed session at the State of Open Con event in London, we brought together an expert panel on the AI Native Dev Podcast to explore this and more: Amanda Brock – CEO of OpenUK, diving into the legal and policy implications of open AI. Richard Sikang Bian – AI expert from Ant Group, sharing insights on China’s AI landscape and DeepSeek’s rise. Guy Podjarny – Founder & CEO of Tessl, challenging the “open” label and exploring its impact on innovation. Simon Maple – Host and tech leader, guiding the discussion on AI transparency and what comes next. 🔍 Key topics we explored: 𝐎𝐩𝐞𝐧 𝐖𝐞𝐢𝐠𝐡𝐭𝐬 𝐯𝐬. 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 – What does “open” really mean in AI? 𝐌𝐨𝐄 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐌𝐨𝐝𝐞𝐥 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧 – How DeepSeek R1 achieved cost-efficient training. 𝐓𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 & 𝐆𝐞𝐨𝐩𝐨𝐥𝐢𝐭𝐢𝐜𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 – Why DeepSeek R1’s release is making waves in AI. 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐎𝐩𝐞𝐧 𝐀𝐈 – Will we ever see truly open models, or is controlled openness the new norm? One key takeaway: Openness in AI is a strategic choice. Some companies embrace transparency, while others see secrecy as a competitive advantage. As AI evolves, the industry must decide whether to lean into collaboration—or double down on exclusivity. 🚀 What do you think? Should AI models be fully open, or is controlled openness the right balance? Drop your thoughts in the comments! 👇 #AI #OpenSource #DeepSeek #MachineLearning #AINativeDev #StateOfOpenCon

What questions would you like me to ask Mati Staniszewski, CEO and co-founder of ElevenLabs, when I interview him on The AI Native Dev Podcast next week? I'm really excited to have Mati on. He's a brilliant, humble and super nice guy, and a very good communicator. If you need more context, ElevenLabs is quite clearly the leader in AI Generated voice. They recently raised $180M Series C with a $3B valuation, have generated over 5,000 *years* worth of audio, and serve millions of users - and the company is less than 3 years old!

Developing with AI ? In NYC Feb 19 ? Our meetup is for you ! You'll learn: What is AI Native Development ? How these tool can benefit from proven techniques like Formal Methods and disciplined refactoring ? Using it in the trenches as part of CLI or autonomous coding. Kindly hosted by Grubhub and supported by the AI Native Developer community. https://lu.ma/ujq988qn

Fine tuning is not effective in learning your codebase - it simply doesn’t change the LLM’s behaviour enough. RAG does - and it adapts as you change code. Guy Gur-Ari, co-founder of Augment Code, gave the best explanation I’ve heard on this topic in our latest podcast episode (with Dion Almaer too!). Check it out here: https://tessl.co/3WGumuG Combined with the Simon Last (Notion co-founder) challenges on the value of fine tuning in a December episode, I’m growing increasingly skeptical of how much value such tuning can bring. If you’re not building a model and have vast data for deep RL, you might as well stick to agents and RAG. The one thing that gives me pause is the wisdom Jason Warner shared when he explained poolside's approach a few episodes back. It may come down to *how* you do post-training, not whether you choose that or RAG. Guy went on to give great and well explained insights on AI dev, combining his long history with ML and LLM with very recent experiments and real world data from Augment. Absolutely loved this episode - a must listen! Would also love to hear opinions and data on RAG vs Fine Tuning in the comment. #AINativeDev #AIDev #RAG #FineTuning #LLM #Agents

We're back with another LIVE session! Join Guy and Simon for a deeper dive into the evolving world of AI Native Development, building on insights from the January episodes of The AI Native Dev Podcast. Mark your calendars for this live event! Stay tuned for guest announcements—you won’t want to miss it!

📽️ New Podcast: The One with Two Guys talking about AI software engineering 📽️ As global as our aperture is, there are constant reminders of how it's a small world. I got to co-host a podcast episode of AI Native Development with two Israeli founders that I have and have had the pleasure of working for... at companies in the space of software development in the new era where computers can communicate with us in natural ways, and can write code. And, Guy Gur-Ari of Augment and Guy Podjarny crossed paths back when they were both in Israel! Guy Gur-Ari has a deep research background, and has built a world class research team at Augment, which I saw first hand. The team is amazing at applying research, and in the episode you get to hear about a lot of the learnings that have come from applying systems that include LLMs to the practice of real world enterprise software. For example, many people talk about the importance of retrieval, but Augment has done the work to lead in this area. Context sure if king, but how do you make sure to get the right context? I also really enjoyed the discussion around fine-tuning and RAG. Once again the details matter. What does it mean to fine-tune a model on your codebase? What does fine-tuning do well in this situation, and where does it fall down? Practically: how do you handle the fact that every engineer is working on their own branch with local changes? And there is so much more in this episode. I think we could have kept rolling for another hour or so for sure... and we discussed open source vs. open weights and how *contribution* is possible or not. How do you "fix a bug" in this world? What does the future look like, and what is the role of autonomous coding? And the biggest question of all: would you want your kids to get a Comp Sci degree these days? Pease join me in your podcast viewing/listening application of choice in the comment below. I'm curious for your thoughts across these topics too! #ainativedev

🚀 𝐖𝐞’𝐫𝐞 𝐛𝐚𝐜𝐤 𝐰𝐢𝐭𝐡 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐋𝐈𝐕𝐄 𝐞𝐯𝐞𝐧𝐭! Join 𝐆𝐮𝐲 𝐏𝐨𝐝𝐣𝐚𝐫𝐧𝐲 and 𝐒𝐢𝐦𝐨𝐧 𝐌𝐚𝐩𝐥𝐞 as they take a deeper dive into the rapidly evolving world of 𝐀𝐈 𝐍𝐚𝐭𝐢𝐯𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭, building on the incredible insights from January’s episodes of 𝑻𝒉𝒆 𝑨𝑰 𝑵𝒂𝒕𝒊𝒗𝒆 𝑫𝒆𝒗 𝑷𝒐𝒅𝒄𝒂𝒔𝒕. 🎙️ Featuring guests like Macey Baker, Quinn Slack, Hannah Foxwell, and Guy Gur-Ari, this live session will unpack key learnings, explore new ideas, and answer your burning questions in real time! 📅 𝐌𝐚𝐫𝐤 𝐲𝐨𝐮𝐫 𝐜𝐚𝐥𝐞𝐧𝐝𝐚𝐫 and don’t miss this exclusive opportunity to stay ahead in AI innovation. Stay tuned—guest announcements are coming soon! 🎧 Catch up on January’s podcast episodes now: (Links in the comments) 👉 𝐒𝐢𝐠𝐧 𝐮𝐩 𝐧𝐨𝐰 on LinkedIn or join us on YouTube to ask your questions live in the comments. Don’t miss it! 🎉

🚀 𝐃𝐨𝐧’𝐭 𝐌𝐢𝐬𝐬 𝐎𝐮𝐭: 𝐀𝐝𝐚𝐩𝐭𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐭𝐞𝐚𝐦 𝐜𝐮𝐥𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐀𝐈—𝐋𝐢𝐬𝐭𝐞𝐧 𝐍𝐨𝐰! As we dive into the AI era, there’s a lot we can learn from the challenges of DevOps adoption. Simon Maple spoke with Hannah Foxwell about how AI will reshape teams and the need to adapt quickly: 𝐓𝐨𝐩-𝐃𝐨𝐰𝐧 𝐌𝐞𝐞𝐭𝐬 𝐁𝐨𝐭𝐭𝐨𝐦-𝐔𝐩: AI adoption has strong backing from leadership, but are developers ready for the pressure? 𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐌𝐢𝐝-𝐋𝐞𝐯𝐞𝐥 𝐃𝐞𝐯𝐬: Senior engineers thrive, and junior devs are "AI natives"—but mid-level devs may need extra support. 𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐁𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤𝐬: With AI speeding up builds, the real bottlenecks are shifting toward prioritization and product strategy. AI is touching every part of the organization, and teams must adapt now. Thanks to Hannah and Simon for an eye-opening conversation! 🎧 (Links in comments) #AI #DevOps #Leadership #AIAdoption #Innovation #TeamCulture