Recent posts
A week has 7 days That means 7 ships

The team has been busy lately. This is what shipped on Replit this week: - Free Mode - Conversations - Routines - Steer conversations - Import Skills from GitHub - Control models across Workspaces - Black-box pen tests
🤯

it's been a wild day to say the least. $24k in revenue from my weekend @Replit project i built on my iphone. grab your spot: http://1milllionpixels.com


You can just make money from your phone!

Update: 53,000 pixels sold on my little app i made on my iphone via @Replit @kensavage is still at the top of the leaderboard! will anyone overtake him? http://1milllionpixels.com


Idea to $$, fast!

woke up to INSANITY!!! look at all these stripe notifications 👀🚀🔥 1. saw http://outbid.lol by @jonathan_wilke 2. built http://1milllionpixels.com on @Replit 3. bought $42 ad 4. sales exploding!!!!!! so many cool companies!


This partnership with @OpenAI is long overdue. Before Replit was in YC, PG asked Sam to recruit us. Here’s Sam telling the story of how we first met:

Replit Free Mode, powered by @OpenAI GPT-5.6 Luna. Let’s make intelligence accessible to everyone.
One of the more underrated aspects of the new Free Mode is how fast it is. Making coding interactive again!

I asked @Replit's AI agent to build me a living medieval town: 200 villagers with names, personalities, jobs, homes, and daily routines. it was free. It took under 6 minutes. I used the new Free Mode for the entire thing. https://medieval-town-simulation.replit.app


You can really build a TON with Free Mode on Replit!

I inadvertently broke out of free mode so it’s not zero $ but I started this project during this morning’s Discord and never maxed out in 5 hours. Free Mode Rocks! @raymmar @manny @Franciscocrz @Replit

Excited for our partnership with OpenAI

Vibe coding company Replit debuted Free Mode today, a new feature powered by OpenAI’s GPT-5.6 Luna model. The joint announcement, shared exclusively with Fortune, heralds an enhanced partnership between the two tech companies that will see them work on several future products. https://t.co/ZaJt2sjrZ9
Agents made software cheaper but made coding expensive. Today, together with @OpenAI, we’re changing this:

Replit Free Mode, powered by @OpenAI GPT-5.6 Luna. Let’s make intelligence accessible to everyone.
Labour to keep alive in your Breast that Little Spark of Celestial fire Called Conscience

This team doesn’t have “AI” anywhere in their pitch but has AI growth rates and would have 10x the headcount if they weren’t so AI-pilled.

Most of the creator economy has been built around reaching people everywhere. Local businesses need something different: creators who live nearby, posting about places their viewers can actually walk into. Today we’re launching @TryNearbyCom as part of @ycombinator S26, live with 120+ paying restaurants across Southern California. We have spent the last 10 months doing restaurant visits with creators and sitting with hundreds of owners, learning what they actually need. That work grew us over 130% since the batch started with over 90% retention since November. Excited to continue to make local businesses discoverable online, and to turn millions of locals into creators for the businesses around them.
It’s not enough to scan your code for vulnerabilities; it’s important to try to break them with pen testing.

You can now run black-box pen tests for your Replit apps. Security scans that test your Replit apps the way external attackers do. Replit Agent can fix what it finds in a single click.
18x improvement in intelligence per joule in 16 months.


hard agree with @amasad —@JonSaadFalcon and my research indicates that intelligence efficiency (intelligence per watt) is rapidly improving and we will definitely *not* need data center scale compute to run agi! links to research in comments below 👇
The argument that “AI structurally centralizes power” because it’s currently compute hungry ignores 125 years of super exponential growth in compute price-performance. Improvement in algorithms and continued hardware efficiency gains means there is no reason to assume AGI-level capabilities will always require a data center to run. Scaling laws are not laws of physics. They’re simply empirical relationships observed for particular architectures, objectives, datasets etc. Change any one of those factors and you get a different scaling curve. If the brain is any guide, true AGI will likely be very efficient. In fact, current scaling laws might a bug and not a feature. It shows how inefficiency of the ML algorithms we discovered so far.


1/2 Thanks Gavin for an especially thoughtful exchange. I don't usually spend much time on social media but I wanted to engage here because it really brings out the heart of an important conversation. First, on regulation, I think that “either concentrate it in the hands of a chosen few companies and politicians via regulation or distribute it widely” is a false choice. I know that there’s a sort of Silicon Valley shorthand where regulation = regulatory capture = concentration of power, but I’ve always found this to be an overly simplified picture of the world. Many people outside this bubble think of regulation as something that constrains corporate power and benefits ordinary people. I don’t necessarily agree with that perspective either, rather I think it’s complicated and really depends on what the “regulation” consists of. But in particular I think that those in the “regulation = regulatory capture = concentration of power” frame often underrate the decentralizing power of objective and fair institutional processes. A crude analogy is that the formal court system can sometimes feel stuffy and elitist, but it does a much better job of defending the rights of vulnerable individuals than the alternative, mob justice. At their best, institutions can vest power in ideas rather than people, and thereby decentralize that power. This is why Anthropic has always made its policy proposals very carefully. We try very hard to make proposals that disadvantage (slow down) frontier AI companies while *advantaging* smaller competitors. California’s SB53 (which we supported), and even the much-maligned SB 1047 (which we were ambivalent on), completely exempt any company below a certain amount of revenue or model training costs from being covered at all (it was $500M for SB 53, lower for 1047 but we objected to that). More recently the testing process we’ve advocated for at CAISI and the White House involves more rigorous tests for frontier models than off-frontier models — something that differentially advantages challengers. Similarly, the “Pacing the Frontier” letter envisions (or at least Anthropic’s preferred implementation of it envisions) modulating the pace of the very best models while not constraining those who are catching up. This hurts the business interests of the frontier labs and helps challengers, including open-weights! Overall my view is that AI is *structurally* a technology that tends to concentrate power, for reasons that have nothing to do with regulation (more to do with the extreme implications of the scaling laws). Open-weights do help some with this but are nowhere near a sufficient solution because they simply shift the concentration somewhat to those with the most compute and chips (which are roughly the frontier labs plus maybe hardware providers). By contrast I think the right “rules of the road” can simultaneously (a) address AI’s cyber/bio/alignment risks, (b) institutionally constrain the power of the frontier AI companies, and (c) leave room for open-weights models while also addressing the specific risks that they bring. BTW I do not think that the events of the last few months have “failed to result in [my] preferred regulatory path”. The approach that the Trump administration is reported to be taking — pre-deployment testing for frontier models, and also testing of open-weights models when they get closer to the frontier — is one that I am very supportive of, though of course I have to see the details to be sure. I am also supportive of Demis Hassabis’ ideas around a FINRA-like entity. This contrasts with six months ago when most of the industry was still pushing for preemption of all state regulation and no apparent federal approach either.
Even if you don’t plan on publishing to the App Store, building personal apps via TestFlight is really great.

in case anyone is curious, https://remoko.app took less than ~50 hours from the first prompt on @replit to sharing the testflight on X


ARC-AGI-3 is nearly solved by merely adding a coding harness. As predicted, coding generalizes LLMs.


I got 96.2% on ARC-AGI-3 with Opus 5, and 99.3% pass@2. The program is basically Claude Code + Opus 5 (high), one action command, and filesystem logs. Almost nothing ARC specific. https://github.com/jerber/arc-code






