Perle brings wisdom to data. Unlock great AI with expert-powered, modular data training solutions that make AI development effortless.
San Francisco, CA
Joined March 2024
- Tweets229
- Following164
- Followers2.7K
- Likes4.7K
Perle retweeted
Been using @JoinProxima for almost 2 years now and these last 3 months have been absolutely insane.
Managed to make ≈$840k, mostly thanks to Robinhood
Genuinely think Proxima has been one of the biggest game changers for devs. The amount of money you can make just by building and launching projects is crazy.
Robinhood was something else though. Really hope it comes back at some point because I haven't had that much fun devving in a long time.
Things have definitely slowed down a lot since then, can't lie.
Been thinking about switching up my approach a bit. Instead of just mass deploying slop every day, I wanna actually take my time with projects and see how far I can push them.
Feel like I've been too focused on getting as many launches out as possible instead of actually trying to make something run up huge.
Would rather put real effort into a few projects and chase millions than keep doing the same thing over and over again.
We'll see how it goes.
proxima.tools/@deployer
Perle retweeted
Had to grind for this shit. shit did not come easy.
proxima.tools/@deployer
Perle retweeted
Been building something for weeks.
Every coin gets the same treatment: dev, holders, bundles, socials. then a verdict.
Almost ready. looking for a few people who live in the trenches to try it first and tell me what I can improve on it. reply or dm
Perle retweeted
If you follow a @sh4vwty callout, you actually need to be publicly executed.
32 year old super torta driller @sh4vwty is the biggest rugger on the pumpfun app.
This is why women shouldn’t have access to the internet or finance. The biggest scammer in this space is an overweight single mother.
Perle retweeted
"The whole concept of saying AI will need zero humans, I 100% disagree with that," said @AhmedZRashad founder and CEO of @Meet_Perle.
Catch the latest episode of Unblock'd with @msjemmagreen.
youtu.be/0iKunYrlnDc?si=zmCR…
Perle retweeted
New York! We’re running it back.
We couldn't be more excited to be co-organizing a Valentine’s Day Hackathon alongside @AnthropicAI, @elevenlabs, @Lovable, and more.
This is @Iterate’s first hackathon of the year with the Columbia CBS AI Club, and it’s going to be a big one.
Expect a full day of building, shipping, and meeting some of the best people in the AI ecosystem.
📍Columbia Business School, NYC
📅 Saturday, Feb 14
Hoping to see you there 🫡
This is a great event to attend. We’ll be hosting at Columbia University along with Anthropocene and Lovable.
Builders in NYC, this one's for you!
The Iterate x @Columbia_Biz's AI Club hackathon is happening on Dec 4 🗽
We're sooo excited to be sponsoring this event alongside @AnthropicAI, @Lovable, @TeamHathora, & @standoutwork.
Not too late to sign up 👇
luma.com/wr3v4hl2
Perle retweeted
This is an excellent history of LLMs, doesn't miss seminal papers I know. Reminds you we're standing on the shoulders of giants, and giants are still being born today. gregorygundersen.com/blog/20…
We recently worked with a healthcare AI company developing autonomous medical agents for note generation. Their goal was to reduce clinician documentation workload and improve record accuracy.
Perle helped strengthen the model architecture, built a benchmark framework with medical experts to capture real-world edge cases, and supported multilingual quality assurance in Arabic, Spanish, and other languages.
The outcome was a more reliable generative stack, clinically relevant evaluation, and broader accessibility for diverse patient populations bringing their medical AI closer to safe, scalable deployment.
Learn more in our full case study: perle.ai/resources/case-stud…
Most AI models fail because of their data.
When annotation is handled by generalists, you end up with:
❌ Higher iteration costs
❌ Missed edge cases
❌ Slower model performance gains
The fix? AI scientists. They bring the domain knowledge, technical context, and precision that transform annotations from “just labels” into the foundation of high-performing, trustworthy models.
At Perle, we don’t just label data. We use experts annotators to ensure high-quality data.
Learn more: perle.ai/resources/revolutio…
Too many AI initiatives start with bold ambitions then derail in weeks as requirements shift, edge cases pile up, and teams scramble to catch up. In annotation-heavy projects, scope creep is especially insidious.
The real costs of loose scoping:
❌ Vague business objectives that leave teams guessing what “success” means
❌ Cross-functional misalignment where data, engineering, and product diverge on priorities
❌ Endless loops: more labels, new categories, rework over rework
❌ Undefined technical specs forcing teams down expensive trial-and-error paths
At Perle, we believe clarity from day one is non-negotiable. We’ve built tools and processes to:
✅ Translate big-picture goals into precise, executable data specs
✅ Align data scientists, engineers, and product owners on the same scope
✅ Minimize rework by locking down requirements early
If your AI project feels like it’s chasing its tail instead of advancing, scope creep is probably the culprit. Learn more: perle.ai/resources/the-hidde…
Smarter AI starts with smarter evaluation. Perle’s fast, expert-driven insights improve and validate your AI models.
We evaluate models based on what really matters:
✅ Fast, expert-in-the-loop assessments
✅ Side-by-side model testing and A/B experiments
✅ Flexible workflows to track accuracy, recall, and key metrics
✅ Expert reviews for bias, safety, and compliance
From early prototypes to production systems, we help you evaluate AI with confidence.
See how it works: perle.ai/solutions
Generic human oversight doesn’t cut it in AI. In high-stakes domains like healthcare, law, and finance, you need experts-in-the-loop.
Here’s why expert guidance matters:
👉 Well-annotated data separates a great model from a mediocre one
👉 Experts catch edge cases that generalists miss
👉 Accurate labels reduce iteration cycles and compliance risk
At Perle, we make sure your data pipelines are guided by domain specialists.
Read more: perle.ai/resources/experts-i…
Where do you see the biggest ROI from investing in data quality?
25%Reducing model drift
25%Faster time-to-market
33%Good customer experience
17%Lower regulatory risk
12 votes • Final resultsHow can you leverage AI in the legal field? We used AI and expert validation to quickly refine workflows and improve data quality for a specialized legal provider.
Here’s how we did it:
We collected and labeled 30K commercial law contracts in Saudi Arabia. Then, we used experts to extract key values, tag every clause, and classify contracts properly.
The new data allowed the model to effectively extract insights, compare contracts, and get suggestions on contract improvements.
Plus, the project was completed in 4 weeks with a 99%+ acceptance rate on all labeled contracts.
Learn more about Perle’s AI solutions: perle.ai/solutions
We’re entering a new era of data labeling—one where models don’t just see, they observe and behave.
At Perle, we believe the next leap in AI will come from observational, human-centric data: multi-modal recordings of how people act, make decisions, and move in real environments. Think real homes, first-person cameras, rich sensor data. It’s the kind of data that lets embodied AI actually imitate human behavior, not just recognize it.
Why this matters:
➕Robots in healthcare, biotech, and the smart industry can’t just be accurate; they must understand context and nuance.
➕Traditional annotated datasets are hitting diminishing returns for embodied AI.
➕Capturing rich, real-world behavior enables models that generalize better, adapt faster, and are safer.
Read our latest blog by Moe Abdelfattah, Head of Product Operations, for more: perle.ai/resources/the-evolu…
