we’re excited to share that we’ve raised $20M (from Accel, Index, & Emergence) to build the 'ai-native Slack' you've all been craving TLDR; ando is a team messaging platform built fresh, from the ground, up for a world where agents act as real collaborators alongside us. we have teams using us across 15 countries, and spanning industries like software, financial services, real estate, & more request access if any of this resonates 💌

"By the end of 2026, agents will read more Stripe docs than humans. Your job is to sell to agents in 2026. You have to be the vendor of choice not just for humans, but for agents." with @MaiaJo_ AI CRO @stripe

Claude Opus 5.5 is the new #1# on APEX-Agents and APEX-Accounting. APEX-Agents: 73.5% Pass@1 (#1#) 81.3% mean score (#1#) APEX-Accounting: 15.4% Pass@1 (#1#) 62.0% mean score (#1#) On APEX-Agents, the new model gains +4.9 pp over Fable 5.1 (68.6%), the previous leader, and +7.6 pp over Opus 5 (65.8%). Anthropic says the biggest gains in this release are on long-running agentic tasks and knowledge work. That is what APEX-Agents measures, and the numbers agree. Opus 5.5 on APEX domains: Management Consulting: 80.0% Pass@1 (#1#) Corporate Law: 71.2% Pass@1 (#3#) Investment Banking: 69.3% Pass@1 (#2#) Accounting: 62.0% mean score (#1#) Token use can indicate domains where the model is most effective. Consulting leads at 80% and uses the fewest tokens at 2.0M per attempt. Law has the highest partial credit (85.7% mean) but costs the most at 4.9M tokens. In Accounting, Opus 5.5 gets partial credit on most tasks but fully passes only 1 in 6. More tokens can increase capability on Opus 5.5. Max effort consumes 3.50M tokens per attempt. That’s 2.1x more than Opus 5 and 1.2x Fable 5.1. But list price fell to $4/$20 per M (Opus 5 was $5/$25), and cache reads are $0.20, so 2.1x the tokens is only about 1.7x the dollars. Effort level has a significant impact on benchmark scores and token usage. Medium effort: 52.3% on 756k tokens Max effort: 73.4% on 3.50M tokens Increasing effort to max gains 21 points for 4.6x the tokens on agentic work. On APEX-Accounting, the same jump buys 2.8 points for 3.4x the tokens. When Opus 5.5 solves a task, it solves it consistently. 142 of 239 tasks passed on all 4 runs. Failing runs used 3.1M tokens vs 1.9M for passing runs, and took twice as long. 13 runs hit a hard failure, all in Corporate Law. 8 of them burned 25M to 42M tokens before dying, showing that long-horizon legal work can still send the model into a loop. Congratulations to the @claudeai team. See full leaderboard:

if you are building a product using AI, you should be spending >25% of your time making benchmarks and trying to get the model labs to care about said benchmarks easiest path to accelerate your progress as a company

Excited to welcome @asadovsky as Harvey’s Chief Research Officer. Before Harvey, Adam co-led post-training at Microsoft AI and Google DeepMind. As a CVP at Microsoft AI, he helped build MAI-Thinking-1, Microsoft’s reasoning model. As part of Gemini’s leadership team he helped train Gemini 1.0 through 2.5, including fine-tuning, RL, data, and evals. His prior work as a Distinguished Engineer at Google spanned Assistant, Search Quality, and Search Infrastructure. I met Adam three years ago when I sent him a cold LinkedIn DM and was surprised he responded. At a time when most dismissed the application layer and legal, Adam was curious and generous with his time. He quickly became someone I regularly turned to for advice on AI as we scaled Harvey over the past three years. When we first met, we were too early to hire someone of his caliber and scale, but I always hoped we’d eventually work together. As Winston and I got to know him better, what stood out even beyond his technical achievements was his character. Despite his incredible technical career, he remains curious, humble, practical, and cares deeply about the teams he builds. We couldn’t think of a better leader to help us build frontier intelligence for the professionals and institutions we serve.