Rami Suliman retweeted
Subtle reminder that you're supposed to feel overwhelmed.
It means you're doing something new. You're taking a new path. You're learning. Growing. You branched into the unknown. It will be frustrating. You will feel lost. It will be painful, but staying the same is even worse.
Rami Suliman retweeted
why is building ai meeting software so essential for any company in the race to create agents, augment the enterprise, etc esp ai labs?
it's really simple. labs are fundamentally context engines. intelligence requires context, esp agentic intelligence. & generally the more of it you have the more useful it becomes.
nearly everything in the modern workplace is already digital in most offices. your emails, documents, messages, code, are all already digitized.
but one of the richest sources of context (spoken context) remained silo'ed or not captured at all. spoken context like this obviously contains extremely important information like reasoning behind decisions, the disgreements that arose, the tradeoffs made, & the long term institional knowedge being generated. remember when we had program managers taking notes in every meeting manually?!
so ppl installed note taking tools, & many billion dollar companies popped up trying to do this (few of them normalized recording without permission to boot). but the problem was that now you have to wrangle third party tools, & mcp's to make it all work together (yuck!). you had an essential utility sitting outside of an ai native suite you already used.
it turns out you would much rather have this context capture mechanic as close to your model as possible because that's literally where everything else is already. it makes it 100x easier to use.
the other more prominent lab should've built meeting software as quickly as possible & made it a primitive but they did not. now you're seeing the new office suites being completed at a break neck pace.
Rami Suliman retweeted
إن المرء لا يرغب في أن يُحب بقدر ما يرغب في أن يُفهم. أن تكون محبوبًا هو أن تكون محاصرًا في صورة صنعها الآخرون عنك. أما أن تُفهم، فذلك هو التحرر من كل الصور.
- هيلمار سودربرغ
Rami Suliman retweeted
ok this is wild, three ex-Apple PhD researchers spent 18 months building an AI that reacts before you've even finished your sentence.
it's called Emma from @nuance_ai. you talk to her face to face, and she listens and reacts while you're still speaking.
and the model behind her, Nuplex, is already available through an API, so your own agents and game characters can have that kind of conversation too.
this is exactly how I imagined talking to AI. meet Emma: app.nuancelabs.ai
🤝 Paid partnership
The first AI persona you’ll actually want to meet face-to-face.
Most AI personas stare blankly while you talk. Emma reacts while you’re still speaking.
After 18 months of research at @nuance_ai, this is Emma, built on Nuplex, our full-duplex audiovisual foundation model.
Rami Suliman retweeted
Your income scales in direct proportion to your intolerance of mediocrity, delay, and status quo.
Rami Suliman retweeted
“Wake up every morning. Do the best you can. Leave no stone unturned. And then let fate do the rest.”
.@NikeshArora grew Palo Alto Networks from $18 billion to $250+ billion in market value. Before that, he spent 10 years helping grow Google’s revenue from $2 billion to $62 billion.
When he joined Palo Alto, he thought cybersecurity was two words. 8 years and 35 acquisitions later, the company has more than 70 products.
His approach: buy the winning team and put its founder in charge. If a startup beat his team with fewer resources, why make its founder report to the person who lost?
Nikesh explains why he makes founders agree to a product roadmap before signing a deal, why he wanted Google to buy Netflix, and how Egon Durban kept him bidding for a cricket team at 6 in the morning.
0:00 The Oculus deal he almost did with Egon Durban
8:45 "I thought cybersecurity was two words"
11:44 Why he buys instead of builds
14:33 Why the acquired founder becomes the boss
16:21 Why he only buys number one or number two
19:30 The 30-day test before every acquisition
33:38 Egon Durban and the 29-round cricket auction
42:32 Why nothing at work stresses him out
44:28 Selling Google ads without knowing the price
49:42 Coming to America with $200
51:58 Raising kids who didn't grow up poor
54:46 Why AI hacking has been good for Palo Alto
Includes paid partnerships.
Rami Suliman retweeted
People keep asking how companies like Ramp, Linear, and Anthropic ship so much software with agents.
There is no new SDLC or secret set of tools. The tools are increasingly available to everyone. You can build your own, use Linear, Claude, or whatever works.
The bigger difference is ambition. These companies encourage their teams to build more, experiment more, and let agents do more of the work. That's the main different between "AI native" or not. You try to use the technology to the max.
But don't get there by adopting a tool. You get there by changing what you believe your teams are capable of.
So in the very simplest way:
- let agents build
- make it easier and faster to contribute
- increase the ambition of what your teams attempt.
In practice, that means things like:
- Standardize around cloud agents. Most of our code (5M lines last quater) is now written in Linear with our Linear cloud coding agent. Cloud sandboxes make standardizing the system and environment easier for everyone and enables faster contributions
- Linear agent (with code access and cloud agent) operates in Slack too. If there is a discussion or someone notices a problem just say "@linear fix this" (decrease the time from something noticed to fixed)
- As bugs, errors etc arrive in Linear, send them to Triage, and have Loop to auto-investigate (connect to tools like Sentry, Datadog), and write a fix
- Let designers, PMs, non-technical people contribute code where useful. If they notice a problem, tell them they can submit a fix. If they want to try something, ask them to build a prototype.
- Set up code review agent and system to make sure PRs are safe to merge.
- Run security as an automation, have agents constantly try to find security issues in your codebase, as they find, send it to a coding agent
- Instead of working on issue level, increase ambition, make a whole project, ask the agent to ask questions, investigate code base, create issues and have the agent work on the whole project at once. Set up goals like "give me proof that what you build works". All our PRs now come with screenshots and videos and preview build you can
- Bring customer feedback to the system like Linear, and make it available for your team. At Linear every project now starts with 10-500 customer notes that influence the direction of the work, and which customers we should ask to test the solution first.
- Set up Loops/automations to constantly update reference documents for agents, like product area/code owners so work can be routed to right humans
Then what is left is how do you set direction for your teams (initiatives, projects they should work on) and how do you review results (better code review, Linear does it with Guided Reviews), how you maintain quality/cohesion which is not a new problem either.
Basically most of the problems you always had remain, but the execution bit is now more and more solved.
At @linear we can help with automation, cloud agents, and code review, customer feedback and planning bits.
But we can't change your mindset or how your company operates.
If your company is still spending energy debating jira workflow steps, you're probably solving the wrong problems.
Rami Suliman retweeted
The older you get, the more you realize how pointless it is to tell people your business. Your plans, money, relationships, and struggles don't need an audience. Move in silence. Privacy is power.
Rami Suliman retweeted
The biggest cheat code for your brain is telling yourself everything will always work out in your favor.
Rami Suliman retweeted
You have to reach the point where your fear of dying mediocre outweighs your fear of looking like an idiot. The first version of anything you do will be embarrassing, that should be obvious, but too many people let that thought keep them the same for decades on end.
Rami Suliman retweeted
Are you measuring outcomes or outputs?
Vanta CEO Christina Cacioppo (@christinacaci) said it right in a recent interview. Every AI company claims to price on outcomes, but most of what gets called an outcome is actually an output: documents drafted, alert closed, account offboard.
An output is something the software did. An outcome is something that changed for the customer because of it.
How they are different:
-- A drafted contract is an output. A deal that closed faster is an outcome.
-- A closed alert is an output. A breach that didn’t happen is an outcome.
-- An offboarded account is an output. Zero lingering access when the auditor shows up is the outcome.
Outputs are easy to count, which is exactly why AI startups bill based on outputs. But billing on outputs rewards volume, not value. Pay per alert closed, and you’ve built a system that’s rewarded for closing alerts, not for making you more secure.
AI Buyers: ask what you’d measure six months from now to know it worked. If the vendor’s pricing unit isn’t tied to that, you’re paying for activity.
AI Startups: real outcome pricing means taking on risk you don’t fully control. That’s hard. Calling per-task pricing “outcome-based” is easy, and customers are starting to notice the difference.
This goes to the recent discussion around CONSEQUENTIAL and VERIFIABLE. Buyers value (and pay the most for) consequential, non-verifiable (long horizon) outcomes.
Outputs are what the software does. Outcomes are why anyone buys it.
Rami Suliman retweeted
Software ate the world, now it's learning to run it. View article
Rami Suliman retweeted
For centuries, the architecture of the firm has barely changed: hierarchies of people organized into departments and coordinated through bureaucracy.
AI agents are about to upend that.
We’re moving toward the self-driving company where humans exercise judgment and agents execute most of the work.
At Replit, we’re building the foundation for this new kind of company. We’re calling it “the Agent Habitat.”
Marc Andreessen wrote a fantastic piece placing this shift in the history of computing. Every major computing revolution began with chaos before businesses figured out how to harness it.
