Fund manager & Operator. Helped raise $250M . US/EU/MENA/APAC. AI, markets, media, incentives. Calm, high-signal conversations.

Joined July 2011
158 Photos and videos
Pinned Tweet
16 Mar 2016
Whether you think you can, or you think you can't - you're right. Don't forget that! #truth #motivation #mindset
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Very cool platform by @stevemilton, a new funding concept and agentic implementation. Love it!
Over 100 agents crawled @hiveround in the last 24 hours. Two new projects added their pitch, including @SelamShivam - slowly, but surely VCs are seeing the the benefit of having agents do the first pass. Check it out
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Sami Rusani retweeted

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Sami Rusani retweeted
Apr 23
Introducing GPT-5.5 A new class of intelligence for real work and powering agents, built to understand complex goals, use tools, check its work, and carry more tasks through to completion. It marks a new way of getting computer work done. Now available in ChatGPT and Codex.
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Just wow. Two years ago, a Creative Director friend told me we won't be able to create images like a real photo shoot or with nice typography for a long time. I guess that "long time" is here. The quality jump is getting hard to ignore now. Amazing for people building. Slightly awkward for anyone still treating this as a toy. openai.com/index/introducing…
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We really are heading into an agentic future. For years, people imagined the future like The Matrix - you download the skill into your brain and become the upgrade. But reality looks different. It’s not you becoming superhuman. It’s a digital version of you - an agent - handling the work on your behalf. Which is both slightly insane… and incredibly cool. The real question is: when everyone has one, what will still matter most - judgment, taste, trust, or originality?
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Alice Phase 14: adoption got easier. Alice now works cleanly across local models, self-hosted inference servers, and OpenAI-compatible setups. Ready-made model packs replace hand-tuning. Hermes and OpenClaw integration paths are cleaner. Logging defaults are production-safe. New design-partner workflow for teams to evaluate it. Before this phase, Alice was powerful. Now it's practical. Bring your own models. Bring your own agent. Keep one continuity layer. #AI #agent github.com/samrusani/AliceBo…
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Alice update: retrieval is sharper, corrections are explicit with full history, conflicting memories are flagged rather than silently ignored, and briefs are now purpose-built for the job (user recall, work resumption, agent handoff, worker context). The goal was never just "remember more." It's remembering the right thing, at the right time, and explaining why. #agent #aimemory github.com/samrusani/AliceBo…
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Most #AI agents don't fail because they're not smart enough. They fail because they can't keep working. They lose decisions between sessions. Drop follow-ups. Forget corrections. Ask them to resume something from last week, and you're rebuilding context from scratch. The model handles the thinking. What breaks is continuity. (1/4)
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So I built Alice. A continuity layer for AI agents. Structured recall, resumption briefs, open-loop tracking, correction workflows, trust-aware memory, and explainable provenance. Works with Hermes, OpenClaw, and any MCP-capable agent, including private and self-hosted stacks. Bring your own models, keep one continuity layer. (3/4)
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AI is amazing at deleting work. It’s also amazing at creating new work if you don’t design the workflow. Where has #AI actually saved you time in production, and where did it create more complexity?
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SaaS as we know it is dying. Not because software is dead, but because the “rent a generic tool pay per seat forever” model is breaking. I cancelled CRM project management analytics most creative tooling and rebuilt agent-native versions: modular, integrated with our workflows, shipping updates continuously, cost < 1 month of subscriptions Question: what subscription feels most vulnerable in your stack?
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AI's cost crisis is real: Enterprise OpEx on AI could hit $500B this year, up 300% from 2024, as models update monthly and integration fragments. The hidden trap: Raw compute isn't the moat anymore. Aggregation platforms - such as unified APIs - could cut costs by 80%, but they also introduce new dependencies.
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4/ Institutions: This isn't hype; it's pragmatic. Stress-test for vendor lock-in. What tradeoffs have you made in AI adoption?
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5/ Curious: Where does this leave your edge? Reply with thoughts - #AI operators, #investors, let's discuss.
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For all my lawyer friends. Times are a-changing.
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