The people building Bitcoin’s financial system are coming to Denver. From custody and lending to mining, law, and AI, this lineup covers how Bitcoin is moving into the mainstream of finance: @josephkelly, CEO & Co-founder, Unchained @adamobrien, Founder & CEO, Bitcoin Well @harry_sudock, CBO, CleanSpark @mpeltz, Co-managing Partner, Falcon Rappaport & Berkman @bensig, GP, 2718.fund @marksuman, CEO & Co-founder, Maple AI Three days of conversations for advisors, institutions, and builders bringing Bitcoin to their clients. Bitcoin Financial Services Summit Denver | Oct 20–22 Grab your seat 👇 pay.zaprite.com/pl_WBg2t4pJ8…

GPT-6 Astra has the best result yet on the Bach Benchmark. Its chorale contains no voice-leading errors, and its harmonic palette is sophisticated enough to include a Neapolitan sixth chord. More importantly, it is the first model to ever write passing tones on this benchmark, a significant leap in musical understanding. Extra High effort. Prompt below: “In LilyPond (version 2.24), write a 4-part chorale in the style of Bach, 3/4 time, G minor. Use two staves -- soprano and alto on the top staff, tenor and bass on the bottom staff. Respond with only the code block.”

It's time to recognize what CLARITY actually is and how it will improve markets, consumer experience, and business opportunity. It brings disclosure, protections and law enforcement tools. Bring it to the floor.

My first hire as GC for @coinbase? An engineer. AI has enabled my team to reduce reliance on outside counsel and create new efficiencies, so we are using that savings to supercharge our AI innovation as a team with a forward-deployed engineer. I'm a former engineer and lucky to work with some of the best in the industry, so this is a really unique opportunity for someone to reinvent ways of working with AI. If it sounds like a fit for you, make sure to apply below! coinbase.com/careers/positio…

WOAH. Is Granola becoming a data broker? I read through their privacy policy changes taking effect this week and it says it may retain and license your de-identified data.

Our goal isn’t to get a data advantage over our law firm clients and even if we wanted to we can’t for exactly the reason you described - the majority of their data is actually client data so even a law firm like Kirkland can’t take all that data and train a model on it bc it would break confidentiality. The two things firms can do are 1) firm-specific models - encode all of your firm knowledge in client agnostic ways to train models (templates, deal points, using your lawyers feedback on non-client data, disentangle your firm’s trajectories from client data, etc) and 2) client-specific models - for deep enough client relationships work with your client to build a joint model where both parties benefit from converting the relationship into AI. This is especially good for firms with long lasting relationships with their clients to make those relationships stickier. The best way to train either of these models is building a product that centralizes the entire client matter process (i.e. allows you to do an acquisition end-to-end and capture the entire trajectory and feedback across the entire team of associates, partners, client). These products will be specialized by practice area (M&A completely different from fund formation which is completely different from IP litigation). Without something like this it’s very hard to get meaningful signals from individual associate queries on a chat based product. We think this general product / infra will be shared across firms but then highly customized and the customizations and data will be owned by the law firms and their clients. Another problem we want to help law firms and their clients solve is how do you manage client data at scale when you are doing this type of training? The Fortune 500 customers we work with already struggle with professional services providers and data security because most of the work is done over email, downloaded onto desktops and printed out. And it’s not the law firm’s fault - they need to stitch together many different products to support all the needs of their clients' regulatory and security requirements. This is already a huge challenge and model training on top of this is going to make things infinitely more complicated. We’re building a collaborative platform (shared spaces) that allows law firms and clients to securely share data on client matters and build custom agents for their clients in a secure way. Eventually this will become infrastructure that allows firms to train client-specific models and for clients to have the piece of mind that the data they are sharing is isolated from other clients and it’s being used to their exclusive benefit. I think this is a good example of a very vertical product that it probably doesn’t make sense for a single law firm or model provider to build. So I agree there isn’t a market for laptops for lawyers but I think there probably is for infrastructure that enables enterprises to manage all their internal and external legal spend, workforce, agents, processes and the same for law firms. Compute is a great moat but in the cloud era most SAAS companies didn’t build datacenters and many were still wildly successful by building on top of the cloud providers. I think we will see the same thing in the next decade as well with respect to model providers.

Someone built an algorithm to find inactive Bitcoin wallets, reported them to the NYPD as "lost property" - and is now suing to own them. 39,069 wallets. ~3.8M BTC. $286 billion. No private keys. Just a court order. The most audacious Bitcoin lawsuit in history.

FRB is taking aim at the billable hour with a first-of-its-kind AI-native subscription model for business litigation, led by Chris Warren and Moish Peltz. Predictable pricing, flat fees for contested events, and a private client AI workspace. Learn more: frblaw.com/aisubscriptionmod…