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followers
16.3K
impressions
6.01M
likes
17.0K
comments
681
posts
292
engagement
0.295%
emv
$125K
Average per post
20.6K

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A&O Shearman was the first law firm to pilot Harvey in late 2022. @GabrielMacht sat down with David Wakeling, the partner and board member who led A&O's early bet on Harvey.
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A&O Shearman was the first law firm to pilot Harvey in late 2022. @GabrielMacht sat down with David Wakeling, the partner and board member who led A&O's early bet on Harvey.
We partnered with @FireworksAI_HQ to train open-source models for legal. Here's what we found: 1) Hybrid legal agents can beat frontier models on quality and cost by routing selectively to a frontier advisor. We tested a hybrid setup where GLM 5.1 served as the primary worker, routing tasks to Opus 4.7 as an advisor when needed. GLM invoked Opus sparingly, just 0.83 times per task on average. The hybrid setup beat Opus on both quality and cost: 18% all-pass vs 14%, at $368 vs $954 across the same 100 tasks. 2) Post-training can push open models to frontier-level legal performance. On a 100-task slice of our Legal Agent Benchmark (LAB), SFT moved Kimi 2.6's all-pass rate from 11% to 15%, beating Opus' 14%. But the cost gap was even more striking: $84 vs $954 across the same 100 tasks, or ~11x cheaper. We're excited to continue working with @FireworksAI_HQ on the next generation of open-source legal agents.
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We partnered with @FireworksAI_HQ to train open-source models for legal. Here's what we found: 1) Hybrid legal agents can beat frontier models on quality and cost by routing selectively to a frontier advisor. We tested a hybrid setup where GLM 5.1 served as the primary worker, routing tasks to Opus 4.7 as an advisor when needed. GLM invoked Opus sparingly, just 0.83 times per task on average. The hybrid setup beat Opus on both quality and cost: 18% all-pass vs 14%, at $368 vs $954 across the same 100 tasks. 2) Post-training can push open models to frontier-level legal performance. On a 100-task slice of our Legal Agent Benchmark (LAB), SFT moved Kimi 2.6's all-pass rate from 11% to 15%, beating Opus' 14%. But the cost gap was even more striking: $84 vs $954 across the same 100 tasks, or ~11x cheaper. We're excited to continue working with @FireworksAI_HQ on the next generation of open-source legal agents.
Would Harvey Specter use Harvey? @GabrielMacht had to ask.
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Would Harvey Specter use Harvey? @GabrielMacht had to ask.
Today we announced a brand partnership with Gabriel Macht. His portrayal of an elite attorney inspired a generation to pursue law. There’s no better partner to support Harvey’s brand growth and the launch of our official Instagram, @ askharvey.
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Today we announced a brand partnership with Gabriel Macht. His portrayal of an elite attorney inspired a generation to pursue law. There’s no better partner to support Harvey’s brand growth and the launch of our official Instagram, @ askharvey.
We’ve acquired Benchmark, an AI platform for investment firms. Together, we’ll build products for our asset manager customers to support the full deal process, from first screen to investment committee. Welcome to Harvey, Benchmark.
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We’ve acquired Benchmark, an AI platform for investment firms. Together, we’ll build products for our asset manager customers to support the full deal process, from first screen to investment committee. Welcome to Harvey, Benchmark.

We're open sourcing a 100M+ token synthetic law firm we built with @EngramLab. The firm contains work product from 250+ synthetic matters across 46 clients, spanning ~10k files. We built this environment to evaluate an agents' ability to search and understand a firm's past practice to inform present work - the same knowledge that a tenured associate or partner would have. It's our first step towards building agents that deeply understand a firm's work and processes. More to come soon Deep dive by @ItsJulioPereyra and @nikogrupen:

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We're open sourcing a 100M+ token synthetic law firm we built with @EngramLab. The firm contains work product from 250+ synthetic matters across 46 clients, spanning ~10k files. We built this environment to evaluate an agents' ability to search and understand a firm's past practice to inform present work - the same knowledge that a tenured associate or partner would have. It's our first step towards building agents that deeply understand a firm's work and processes. More to come soon Deep dive by @ItsJulioPereyra and @nikogrupen:
Harvey has raised a $160M Series F, led by @a16z, with participation from @wndrco, @sequoia, @kleinerperkins, @conviction & @eladgil.
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Harvey has raised a $160M Series F, led by @a16z, with participation from @wndrco, @sequoia, @kleinerperkins, @conviction & @eladgil.

x.com/i/article/207753626…

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http://x.com/i/article/2077536260474933248
We're open-sourcing 10 RL environments for M&A due diligence. In our LAB: Diligence environments, agents do research in a virtual data room (VDR) and draft a diligence memo for a merger or acquisition. The largest of these environments contains 80M tokens of context with 1,000 grading criteria. M&A diligence is one of the most difficult tasks for frontier agents due to: 1) Long context: A single thread of VDR research may involve hundreds of documents in a dataroom that contains hundreds of thousands. 2) Exhaustivity: every document must be searched for needle-in-a-haystack provisions that kill a deal. 3) Data: M&A diligence data is privately held by transactional law firms - models aren't trained to do it. It is also one of the world' most valuable knowledge work tasks: global M&A volume is ~$4T annually, of which ~1% is spent on diligence. Deep dive below by @ItsJulioPereyra and @nikogrupen:
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We're open-sourcing 10 RL environments for M&A due diligence. In our LAB: Diligence environments, agents do research in a virtual data room (VDR) and draft a diligence memo for a merger or acquisition. The largest of these environments contains 80M tokens of context with 1,000 grading criteria. M&A diligence is one of the most difficult tasks for frontier agents due to: 1) Long context: A single thread of VDR research may involve hundreds of documents in a dataroom that contains hundreds of thousands. 2) Exhaustivity: every document must be searched for needle-in-a-haystack provisions that kill a deal. 3) Data: M&A diligence data is privately held by transactional law firms - models aren't trained to do it. It is also one of the world' most valuable knowledge work tasks: global M&A volume is ~$4T annually, of which ~1% is spent on diligence. Deep dive below by @ItsJulioPereyra and @nikogrupen:
The best legal teams aren't using AI to replace lawyers’ time. They're using AI to reclaim it for judgment, strategy, and collaboration. AI agents run the workflows. Lawyers drive the outcomes. Harvey is the platform where both happen. Today we announced new funding led by GIC and Sequoia to scale the agents our customers run on Harvey and expand the legal engineering teams that help them turn expertise into systems. Read more: https://t.co/hotheA8LFv
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The best legal teams aren't using AI to replace lawyers’ time. They're using AI to reclaim it for judgment, strategy, and collaboration. AI agents run the workflows. Lawyers drive the outcomes. Harvey is the platform where both happen. Today we announced new funding led by GIC and Sequoia to scale the agents our customers run on Harvey and expand the legal engineering teams that help them turn expertise into systems. Read more: https://t.co/hotheA8LFv
Heading into the long weekend like…
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Heading into the long weekend like…

Introducing Harvey Research: We've shared our model strategy. We've open-sourced Legal Agent Benchmark, the largest benchmark for long-horizon legal work spanning 1,200 tasks across 24+ practice areas. And we've collaborated on research with leading neolabs and inference providers like @baseten, @trajectorylabs, @LangChain, @FireworksAI_HQ, @appliedcompute, and @EngramLab. Now we have a home base for it. Live at: harvey.ai/research

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Introducing Harvey Research: We've shared our model strategy. We've open-sourced Legal Agent Benchmark, the largest benchmark for long-horizon legal work spanning 1,200 tasks across 24+ practice areas. And we've collaborated on research with leading neolabs and inference providers like @baseten, @trajectorylabs, @LangChain, @FireworksAI_HQ, @appliedcompute, and @EngramLab. Now we have a home base for it. Live at: http://harvey.ai/research

t.co/hlb6DJ8zbV

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https://t.co/hlb6DJ8zbV

Harvey is scaling quickly, and we’re expanding our team. 2025 highlights: - Used by 1000+ firms in 59+ countries - 500+ employees - $190M+ in ARR As we look ahead, Harvey is hiring for several high-impact roles. See our open roles here: t.co/ZQtzZBzUlm

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Harvey is scaling quickly, and we’re expanding our team. 2025 highlights: - Used by 1000+ firms in 59+ countries - 500+ employees - $190M+ in ARR As we look ahead, Harvey is hiring for several high-impact roles. See our open roles here: https://t.co/ZQtzZBzUlm
Law school is changing. @GabrielMacht sat down with students at three of our law school partners to talk about how they use Harvey.
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Law school is changing. @GabrielMacht sat down with students at three of our law school partners to talk about how they use Harvey.

How does a seasoned Supreme Court lawyer prepare for the biggest case of his life? Using Harvey. Read how Harvey supported @neal_katyal in refining his arguments before the Supreme Court and how we are bringing those tools to law schools with Harvey Moot: t.co/r3IIKiTwxV

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How does a seasoned Supreme Court lawyer prepare for the biggest case of his life? Using Harvey. Read how Harvey supported @neal_katyal in refining his arguments before the Supreme Court and how we are bringing those tools to law schools with Harvey Moot: https://t.co/r3IIKiTwxV
Anthropic announced at Enterprise Agents Day that Harvey is part of its MCP program. The new Harvey Claude connector gives professionals direct access to Harvey inside Claude and Claude Cowork. Agentic workflows are rapidly becoming real. We’re proud to be part of this launch and to bring Claude connectivity to our customers. Read more: https://t.co/q5WLdOKULr
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Anthropic announced at Enterprise Agents Day that Harvey is part of its MCP program. The new Harvey Claude connector gives professionals direct access to Harvey inside Claude and Claude Cowork. Agentic workflows are rapidly becoming real. We’re proud to be part of this launch and to bring Claude connectivity to our customers. Read more: https://t.co/q5WLdOKULr
Last night, Harvey signed our 101st Am Law 200 firm, so we now serve the majority of Am Law 200 and 60+ of the Am Law 100 firms. Job's Not Finished.
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Last night, Harvey signed our 101st Am Law 200 firm, so we now serve the majority of Am Law 200 and 60+ of the Am Law 100 firms. Job's Not Finished.
Introducing Contract Intelligence. Contract agents take first pass on inbound contracts, apply your playbooks, and generate redlines. See how clauses and negotiated positions are trending across your business. Escalate what needs legal judgment, while agents handle the rest.
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Introducing Contract Intelligence. Contract agents take first pass on inbound contracts, apply your playbooks, and generate redlines. See how clauses and negotiated positions are trending across your business. Escalate what needs legal judgment, while agents handle the rest.
We're partnering with @trajectorylabs to bring sovereign continual learning to legal AI with NVIDIA Nemotron models. Continual learning allows agents to improve over time from feedback on their work: every redline refines the next draft. Open-weight models offer full auditability and data sovereignty over legal agents. Using Trajectory's platform, we post-trained NVIDIA Nemotron 3 Super on our Legal Agent Benchmark (LAB), measuring performance on 1,200+ complex end-to-end legal tasks across 24 practice areas. Initial results show that a post-trained Nemotron 3 Super can match performance of closed-source frontier models. This is just the start: we'll keep pushing the frontier with the more powerful Nemotron 3 Ultra when available.
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We're partnering with @trajectorylabs to bring sovereign continual learning to legal AI with NVIDIA Nemotron models. Continual learning allows agents to improve over time from feedback on their work: every redline refines the next draft. Open-weight models offer full auditability and data sovereignty over legal agents. Using Trajectory's platform, we post-trained NVIDIA Nemotron 3 Super on our Legal Agent Benchmark (LAB), measuring performance on 1,200+ complex end-to-end legal tasks across 24 practice areas. Initial results show that a post-trained Nemotron 3 Super can match performance of closed-source frontier models. This is just the start: we'll keep pushing the frontier with the more powerful Nemotron 3 Ultra when available.

Harvey (@harvey) X Stats & Analytics

Harvey (@harvey) has 16.3K X followers with a 0.29% engagement rate over the past 12 months. Across 292 posts, Harvey received 17.0K total likes and 6.01M impressions, averaging 58.2 likes per post. This page tracks Harvey's performance metrics, top content, and engagement trends — updated daily.

Harvey (@harvey) X Analytics FAQ

How many X (Twitter) followers does Harvey have?+
Harvey (@harvey) has 16.3K X (Twitter) followers as of August 2026.
What is Harvey's X (Twitter) engagement rate?+
Harvey's X (Twitter) engagement rate is 0.29% over the last 12 months, based on 292 posts.
How many likes does Harvey get on X (Twitter)?+
Harvey received 17.0K total likes across 292 posts in the last 12 months, averaging 58.2 likes per post.
How many X (Twitter) impressions does Harvey get?+
Harvey's X (Twitter) content generated 6.01M total impressions over the last 12 months.