<div>Subject: 82% of Americans want to slow AI development<br></div><div dir="auto">Plus, grokking</div><div><div class="gmail_quote"><u></u><div class="notranslate">
<div style="display:none;font-size:0px;line-height:0px;max-height:0px;max-width:0px;opacity:0;overflow:hidden">52% think there needs to be government regulation and a vast majority more believe large tech giants can't be trusted to self regulate </div>
<div style="display:none;max-height:0px;overflow:hidden">
<br>
</div>
<table align="center" class="m_-6771087747756058669document"><tbody><tr><td valign="top">
<table align="center" border="0" cellpadding="0" cellspacing="0" class="m_-6771087747756058669container" width="600"><tbody><tr><td>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td bgcolor="" class="m_-6771087747756058669container">
<table width="100%"><tbody><tr><td class="m_-6771087747756058669container">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" style="margin-top:0px" width="100%"><tbody><tr><td style="padding:0px">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div style="text-align:center"><span style="margin-right:2px"><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftldr.tech%2Fai%3Futm_source=tldr/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/cB5jq03vStJ7S39I5BaHu48K-V6rrvWSIyTCHNbzrss=313" rel="noopener noreferrer" target="_blank"><span>Sign Up</span></a></span>|<span style="margin-right:2px;margin-left:2px"><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftldr.tech%2Ftalent/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/8k-k1bBO5qVVou7Jvrzeua0e3KOgTR-C1Nz98gXJq3Q=313" rel="noopener noreferrer" target="_blank"><span>Jobs</span></a></span>|<span style="margin-right:2px;margin-left:2px"><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fshare.hsforms.com%2F1OxvmrkcFS4qsxKpNXCi76wee466%3Futm_source=tldrai%26utm_medium=newsletter/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/s8s9_E4kg-OftB1j4QaOafHBR0uRL193gXFeoMPr5Q4=313" rel="noopener noreferrer" target="_blank"><span>Advertise</span></a></span>|<span style="margin-left:2px"><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Factions.tldrnewsletter.com%2Fweb-version%3Fep=1%26lc=078d99d6-b44d-11ed-ba38-55928061a93d%26p=99659d9c-3763-11ee-aabc-3f083537c2b1%26pt=campaign%26t=1691672817%26s=ca4fb3b684b2c28331b923e99bcd4f478c0ec0a39dac024d2860187b31534aed/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/94gnuRR-rR88YQQNNxZ87GBhiTFS-o0uXnod6ybCxmw=313" target="_blank"><span>View Online</span></a></span>
<br>
</div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="text-align:center"><span style="color:rgb(51,175,255)!important;font-size:30px">T</span><span style="font-size:30px"><span style="color:rgb(232,192,96)!important;font-size:30px">L</span><span style="color:rgb(101,195,173)!important;font-size:30px">D</span></span><span style="color:rgb(220,107,107)!important;font-size:30px">R</span>
<br>
</td></tr></tbody></table>
<table style="table-layout:fixed;width:100%" width="100%"><tbody><tr><td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0">
<div style="text-align:center">
<h3><strong>TLDR AI 2023-08-10</strong></h3>
</div>
</td></tr>
<tr></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr>
<tr bgcolor=""><td class="m_-6771087747756058669container">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td style="padding:0px">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center"><span style="font-size:36px"><span style="font-size:36px">🚀</span></span></div></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center">
<h3><strong>Headlines & Launches</strong></h3>
</div>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.axios.com%2F2023%2F08%2F09%2Fai-voters-trust-government-regulation%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/WzT45lMQ6Q1ThU-iZvTX7wAgIBJ0C6WSzjyuoY0021k=313" target="_blank"><span><strong>82% of Americans think we should slow down AI development (7 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">In a new Axios survey of 1001 people across one week in July, participants expressed their opinions on a variety of topics around AI safety and capabilities development. 52% think there needs to be government regulation and a vast majority more believe large tech giants can't be trusted to self regulate.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftechcrunch.com%2F2023%2F08%2F09%2Fanthropic-launches-improved-version-of-its-entry-level-llm%2F%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/icXlXnNyaMPbACFbQT9aCSura7JbfSzHX6NU2KSQEuo=313" target="_blank"><span><strong>Anthropic Launches Improved Version Of Its Entry-Level LLM (3 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Anthropic has released Claude Instant, an updated version of its faster, cheaper, text-generating model. Claude Instant generates longer, more structured responses, follows formatting instructions better, and shows improvements in quote extraction, multilingual capabilities, and question answering. It is available through an API.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Finworld.ai%2Fblog%2Finworld-valued-at-500-million%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/mOnGCEd-BvyfPtCgqAcOUlYdioRS-S8pcgjzfJU9Xu0=313" target="_blank"><span><strong>Inworld AI Becomes the Best-Funded Startup in AI x Gaming (8 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Inworld AI announced a new $50M+ round led by Lightspeed Venture Partners, bringing the total valuation of the company to over $500M. This will allow Inworld to accelerate R&D efforts, hire top talent, build a more robust Character Engine, expand infrastructure, and open source parts of its platform.</span></span></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center"><span style="font-size:36px">🧠</span></div>
</div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center">
<h3><strong>Research & Innovation</strong></h3>
</div>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpair.withgoogle.com%2Fexplorables%2Fgrokking%2F%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/dozd6dfBnYT3ssIDTzbWjvlxPmX5h09d6nkY7UmhqHo=313" target="_blank"><span><strong>Understanding Grokking (21 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">The PAIR group at Google has released a lovely explainer that goes deep into the topic of Grokking. Grokking is the dynamic process a model goes through during training that may point to a shift from memorizing to understanding. It isn't well understood in general, but this is a lovely introduction that covers much of the groundwork for this strange phenomenon.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Farxiv.org%2Fabs%2F2308.03977%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/wulc6oidf-Zzm7boadMkincwnWptBA1tz0BVvT8ICq4=313" target="_blank"><span><strong>Photorealistic synthetic data in unreal engine (17 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Don't have millions of photorealistic images to train your algorithm? Maybe you can generate them using PUG from Meta AI. It uses the powerful unreal game engine in a controllable way to generate synthetic image data for downstream training.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Farxiv.org%2Fabs%2F2308.03958%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/1JZzu5MKorx2N7NPgYLwPl4Hn5NpoKeKATQA5le9I7I=313" target="_blank"><span><strong>Simple synthetic data reduces sycophancy (23 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Sycophancy is when a model repeats and adopts a user's opinion. This happens more in larger models and instruction-tuned models. It can also occur in tasks when the opinion is irrelevant, leading to bubble-like behavior. Simple synthetic data fine-tuning can prevent this without harming overall performance.</span></span></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center"><span style="font-size:36px">🧑💻</span></div>
</div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center">
<h3><strong>Engineering & Resources</strong></h3>
</div>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.qawolf.com%2F%3Futm_campaign=Get80PercentEndToEnd08102023%26utm_source=tldrai%26utm_medium=newsletter/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/ai2C1v-ycFgFoiJx6BMTMSoYiO5GFIgmUZrUcod3PwE=313" target="_blank"><span><strong>Get 80% end-to-end test coverage in 4 months (Sponsor)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">There’s a shortcut to reaching high automated test coverage, without spending years on scaling in-house teams. The answer? It's not AI. It's <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.qawolf.com%2F%3Futm_campaign=Get80PercentEndToEnd08102023%26utm_source=tldrai%26utm_medium=newsletter/2/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/9Y2VqCrz4roH97LPqoNFUuV4E0-KgEK_PMsGWB2_wsk=313" rel="noopener noreferrer nofollow" target="_blank"><span>QA Wolf.</span></a>
<br>
<br>QA Wolf gets you to <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.qawolf.com%2F%3Futm_campaign=Get80PercentEndToEnd08102023%26utm_source=tldrai%26utm_medium=newsletter/3/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/DsU6hNSbtKDCjiK2oeTKJfdiJJyjwI8UMA7AEEMtKwI=313" rel="noopener noreferrer nofollow" target="_blank"><span>80% automated end-to-end test coverage in 4 months</span></a>. Plus, they do all the test maintenance, provide unlimited parallel test runs on their infrastructure, and send human-verified bug reports directly to your issue tracker.
<p>Skeptical? <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.qawolf.com%2F%3Futm_campaign=Get80PercentEndToEnd08102023%26utm_source=tldrai%26utm_medium=newsletter/4/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/OB3qWL4w1V4DraGGqz879KPxovkVl_RncsCZLUjYIFU=313" rel="noopener noreferrer nofollow" target="_blank"><span>Schedule a demo to learn more.</span></a>
<br>
<br>PS: QA Wolf has a 4.8/5 ⭐ rating on G2 and they have multiple case studies of customers saving $480k+ on QA engineering.</p>
</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Famirmansurian%2Faicsd%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/ldvH9XqQJcPsIwA9uLCL62aoWWIWV9-sKldfFjaLG-Q=313" target="_blank"><span><strong>A New Method for Improving Student Networks in Computer Vision (GitHub Repo)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Deep neural networks have excelled in computer vision, but faster inference times are needed. This paper introduces the Inter-Class Similarity Distillation method and an Adaptive Loss Weighting strategy for better knowledge transfer from a teacher network to a student one.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Frcgai%2Fsimplyretrieve%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/fmCDfhKahpG1e0uqXWcfV665Cb6VjXoS7kneexv1HMU=313" target="_blank"><span><strong>Integrate Private Data into LLMs while Preserving Privacy (GitHub Repo)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Generative AI systems have grown with the help of Large Language Models. The SimplyRetrieve open-source tool offers a user-friendly way to integrate private data into these systems without extra tuning using the Retrieval-Centric Generation approach. It promises enhanced AI performance while ensuring privacy.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Fjackmpcollins%2Fmagentic%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/6-Kyg0ZdD6jFGKhUFHFTO3trKF_YyLJp_JW6QjY-7dA=313" target="_blank"><span><strong>Magentic (GitHub Repo)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Magentic makes it easy to integrate Large Language Models (LLMs) into your Python code. Treat prompt templates as functions, using type annotations to specify structured output. Then, seamlessly mix LLM queries and function calling with regular Python code to create complex LLM-powered functionality.</span></span></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center"><span style="font-size:36px">🎁</span></div></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center"><strong><h3>Miscellaneous</h3></strong></div>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Findianexpress.com%2Farticle%2Ftechnology%2Fartificial-intelligence%2Fgoogles-brain2music-ai-can-listen-to-your-brain-signals-to-reproduce-music-you-listened-to-8882357%2F%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/jQnEtFHxjAd0ncAdsn7TckD_3Ir4dMyQ65gBGvTTJeo=313" target="_blank"><span><strong>Google Is Working On ‘Brain2Music’ (2 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Google is working on a new AI called ‘Brain2Music’ that uses brain imaging data to generate music. Researchers say the AI model can generate music that closely resembles parts of songs a person was listening to when their brain was scanned.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.engadget.com%2Fthe-white-houses-ai-cyber-challenge-aims-to-crowdsource-national-security-solutions-170003434.html%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/4S1gpsI_f0dLjpo8fpeRpM9BvsjfQntl1a1GyFv4aS0=313" target="_blank"><span><strong>White House Announces ‘AI Cyber Challenge’ (3 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">The Biden Administration revealed its plans to better defend the nation’s critical digital infrastructure at the Black Hat USA Conference in Las Vegas on Wednesday: it's launching a DARPA-led challenge competition to build AI systems capable of proactively identifying and fixing software vulnerabilities.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.briankitano.com%2Fllama-from-scratch%2F%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/AFexqBn3m4gaCzGKMKbF_NNgRjb6fLGetc13B6StGPU=313" target="_blank"><span><strong>Llama From Scratch (20 minute read)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">A step-by-step guide for using the Llama paper to train TinyShakespeare.</span></span></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center"><span style="font-size:36px">⚡</span></div></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding-top:0px;padding-bottom:0px">
<div>
<div style="text-align:center">
<h3><strong>Quick Links</strong></h3>
</div>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.parea.ai%2F%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/yF_IXy7dVl3zMQ7Pn-G9QiaoKDykBWqek-W6_QRaHRY=313" target="_blank"><span><strong>Parea AI - the developer toolkit for debugging and monitoring LLM apps (Product)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Experiment with prompts & model configurations in a versioned manner. Evaluate prompts with custom-defined Python evaluation metrics on a large scale. Monitor LLM applications via API and view analytics on a dashboard.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcolab.research.google.com%2Fdrive%2F1Zmaceu65d7w4Tcd-cfnZRb6k_Tcv2b8g%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/IAEuj5P2bWHCwCD_n7K5P7UCO18oF4R-YYNtYCAXtzk=313" target="_blank"><span><strong>Fastest way to tune Llama (Colab Link)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Upload your JSONL data to your drive, link it, and run this notebook with QLoRA and SFT training to get a custom-tuned Llama2 model. This seems to be the most minimal example I have found for tuning and works well. Most importantly, the model uses a (prompt, response) format.</span></span></div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div><span><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Fmquan%2Fapi2ai%3Futm_source=tldrai/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/BH9aewngOB74RDJEcUYeOX6FQFK544EyUYA3eOHwgsg=313" target="_blank"><span><strong>api2ai (GitHub Repo)</strong></span></a>
<br>
<br><span style="font-family:"Helvetica Neue",Helvetica,Arial,Verdana,sans-serif">Create an API assistant from any OpenAPI spec.</span></span></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div>
<p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fdanni763618.typeform.com%2Fto%2FrSL4lOH3/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/op6ZB4zBM48bIM7m0OX_8C54p0Ffs8Xec0SFgiGn9hw=313" target="_blank"><strong><span>TLDR Talent</span></strong></a> is our exclusive community where we help world-class tech talent and get intros to companies of their choice, along with a number of exciting startups and tech companies curated by TLDR.</p>
<p>We give you full control of the process, you can specify if you’re actively searching or passively interested only if something amazing comes along. Set your preferred compensation, seniority/title/role, specific companies (or types of companies) you’d like to work for and more. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fdanni763618.typeform.com%2Fto%2FrSL4lOH3/2/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/P0wmI8_sCAQ0JfAEMND9VGkkqjcCtegKafp-7liK4K0=313" target="_blank"><strong><span>Click here to apply</span></strong></a>.</p>
</div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<br>
<div>If your company is interested in reaching an audience of AI professionals and decision makers, you may want to <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fshare.hsforms.com%2F1OxvmrkcFS4qsxKpNXCi76wee466%3Futm_source=tldrai%26utm_medium=newsletter/2/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/q6zqoX5qToBbuOIOyTWvQJR37O0Hn7ONw4g1c3aYjEg=313" target="_blank"><strong><span>advertise with us</span></strong></a>.</div>
<br>
<div>If you have any comments or feedback, just respond to this email!
<br>
<br>Thanks for reading,
<br><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftwitter.com%2Fandrewztan/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/W7Ru1xITJRL5CyGHY8MK_jk5yL9_aqm4FB4zzw5pGRc=313" target="_blank"><span>Andrew Tan</span></a> & <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftwitter.com%2Fandrew_n_carr/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/DBxT5O5O4MIVUp0lDqsl7B3bEjU8aFwj-S5BGZ9oJPY=313" target="_blank"><span>Andrew Carr</span></a>
<br>
<br>
</div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="m_-6771087747756058669container" style="padding:15px 15px">
<div>If you don't want to receive future editions of TLDR AI, please <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Factions.tldrnewsletter.com%2Funsubscribe%3Fep=1%26l=eedf6b14-3de3-11ed-9a32-0241b9615763%26lc=078d99d6-b44d-11ed-ba38-55928061a93d%26p=99659d9c-3763-11ee-aabc-3f083537c2b1%26pt=campaign%26pv=4%26spa=1691672427%26t=1691672817%26s=c23cc4a82d95bc0903338a78a6a761a7e1dcb8915c8143cb36d3657675c5c3db/1/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/lu_8pQTUsDj7rr50kg5WmreZtHeRBjqs1LRueSO5Mgk=313" target="_blank">click here to unsubscribe</a>.
<br>
<br>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
<img alt="" src="http://tracking.tldrnewsletter.com/CI0/01000189df8eaf35-1dc6d6f4-a4e1-4e84-bed0-0a1ac5ea1bf9-000000/HNVN2vip-OBmNXn6JCwjGcZgilHQBKtcsclB92_nR2Y=313" style="display:none;width:1px;height:1px">
</div></div></div>