Latent.Space ✓
@latentspacepod · youtube.com/@latentspacepod · joined 19 Jun 2023
AI Engineering podcast, newsletter & community. Technical news today you’ll use at work tomorrow. Business: business@latent.space · Tips: tips@latent.space
31 954Followers
127Following
2 054Posts total
2.5MViews on collected posts
Latest posts
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@latentspacepod @OpenAI c'mon! "$6 an hour" is blatantly clickbait. you can and should do better!
4.4K views · 5 likes
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@latentspacepod @OpenAI $6/hr wall-clock is the sticker. Continuous runs bill watts × hours × $/MWh at the rack, not token list price. Have you published joules-per-accepted-PR (or per merged eval), or only API $/hour?
5.2K views · 4 likes
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@latentspacepod @OpenAI hire an automated AI engineer for under $6/hr is the pitch that finally makes agent budget a line item instead of a vibe. the interesting question is which of those tasks still need a human in the loop at hour 4.
1.1K views · 3 likes
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GitHub invented pull requests, and for 18 years they've been open by default. But now some of the top AI-native open source projects are shutting PRs off, because they've found a better way. https://t.co/zkd7UXo6lo
2.1K views · 7 likes
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Lovable is a $13B company with a $500 million annualized revenue run rate. It's just 3 years old, but essentially competes with Vercel & Cloudflare now — amazing story. And now @Lovable is branching out from AI-powered web app creation and into MCP-powered ‘capabilities’.
We htt
7.3K views · 6 likes
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🆕 We’re launching a new Forward Deployed Engineering pod with @realbasilchatha!
https://t.co/znYKa8xiMe
In just the last few months, $9B+ has been committed by OpenAI, Anthropic, Google, and Microsoft to deploy AI into the enterprise. Model capabilities are there, but it takes
4.9K views · 24 likes
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Models keep absorbing the harness into their weights — soon, it will be a harness for human attention rather than for the model.
A thoughtful analysis of the past and future of agent harnesses by @daniel_mac8 https://t.co/1C9mnPlxpb
3.9K views · 42 likes
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@latentspacepod @swyx @GraySwanAI @zicokolter When auditing severe vulnerabilities with AI agents, the chat context becomes massive. Standard text search completely fails to capture the multi-dimensional reasoning logic.
19 views · 0 likes
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@latentspacepod @GraySwanAI @zicokolter I hope corps really pay attention to this stuff, because i'm no opsec wizard lol
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@latentspacepod @GraySwanAI @zicokolter the scary part isn't jailbreaking the model. it's what the agent can actually touch once it acts. real attack surface is permissions, not prompts
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Gray Swan: Red-Teaming after Mythos & the coming AI security crisis https://t.co/hpICIDksAx
@GraySwanAI cofounders @zicokolter and Matt Fredrikson explain why AI security is fundamentally different from traditional cybersecurity, how their automated red-teaming system Shade can
1.8M views · 25 likes
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0:21
@simonw @ankitxg nice followup from Ankit: https://t.co/R0eZ77SxCE
and @TheNoahHein : https://t.co/FPa1iubJLu
and https://t.co/pZxB9y6Sps
it is coming!
2K views · 4 likes
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@latentspacepod @simonw @ankitxg The solution to "too many PRs and they are too big to review" is not to get humans out of the loop. At least not if the PRs are generated by unreliable AI that cannot be verified otherwise.
If we want to use AI to generate code that can be verifi
5.1K views · 35 likes
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🆕 How to Kill The Code Review
https://t.co/URP7WmtrnZ
the volume and size of PRs is skyrocketing. @simonw called out StrongDM’s “Dark Factory” last month: no human code, but *also* no human review (!?)
in this week’s guest post, @ankitxg makes a 5 step layered playbook for
619.1K views · 796 likes
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Against accounts of the same size
7 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.
Median views4 398this account924median for 10K–100K
Reach, %13.76%this account3.62%median for 10K–100K
Engagement, %0.26%this account1.52%median for 10K–100K
| Metric | This account | Median for 10K–100K | Ratio |
|---|---|---|---|
| Median views per post | 4 398 | 924 | 4.76× |
| Reach (views ÷ followers) | 13.76% | 3.62% | 3.80× |
| Engagement rate | 0.26% | 1.52% | 0.17× |
Others in this range → Compare with another account → How these benchmarks are built →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
5.1K3 Mar
114.4K27 Mar
2K
1.8M22 Jun
27
220
19
3.9K22 Aug
4.9K25 Aug
7.3K26 Aug
2.1K1 Sep
1.1K3 Sep
5.2K
4.4K4 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.82%3 Mar
0.23%27 Mar
0.25%
0.00%22 Jun
0.00%
0.00%
0.00%
1.41%22 Aug
0.60%25 Aug
0.11%26 Aug
0.63%1 Sep
0.26%3 Sep
0.08%
0.14%4 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes36.2%1 169 in total
Reposts4.6%148 in total
Replies2.6%85 in total
Quotes2.2%71 in total
Bookmarks54.3%1 753 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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