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    <title>Onur Solmaz blog</title>
    <description>Explorations in software, agentic systems, math, languages and more.</description>
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    <pubDate>Thu, 13 Aug 2026 15:54:47 +0000</pubDate>
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        <title>😎</title>
        <description>😎
https://t.co/xxfnJQ9MN3</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
        <category>tweet</category>
        <pubDate>Thu, 13 Aug 2026 05:42:36 +0000</pubDate>
        <link>https://solmaz.io/x/2087776814651338815/</link>
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        <title>A similar thing can be done with codex session compaction summaries as well btw</title>
        <description>A similar thing can be done with codex session compaction summaries as well btw

I had verified the other day that a compaction summary created by one account can be used by another account if it has the encrypted blob

ie. they were not keyed/guarded with your account id</description>
        <dc:creator>Onur Solmaz</dc:creator>
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        <pubDate>Wed, 12 Aug 2026 10:19:51 +0000</pubDate>
        <link>https://solmaz.io/x/2087484201419334104/</link>
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        <title>This type of estimations are useful not only for local but for all inference providers</title>
        <description>This type of estimations are useful not only for local but for all inference providers

Get a ballpark of max possible throughput for a model, directly calculate your revenue</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
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        <pubDate>Wed, 12 Aug 2026 03:36:36 +0000</pubDate>
        <link>https://solmaz.io/x/2087382719940501673/</link>
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        <title>mfw trying not to think about the 100 models that dropped this week</title>
        <description>mfw trying not to think about the 100 models that dropped this week

I’ll be offline 1 week to touch grass and reset

I’ve literally not given any break since claude code came out lol

let’s see if I can resist the urge to check twitter</description>
        <dc:creator>Onur Solmaz</dc:creator>
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        <pubDate>Tue, 11 Aug 2026 01:08:32 +0000</pubDate>
        <link>https://solmaz.io/x/2086983068959879561/</link>
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        <title>Deterministic monitors make long-running agents reliable</title>
        <description>I&#39;ve felt the lack of a certain feature of codex desktop app since I went back to the CLI: scheduled tasks

Codex desktop app can keep track of a task until it is properly finished. It&#39;s basically cron. And for some reason, codex CLI still doesn&#39;t have it. Codex app acts as a shared runtime, and for some reason, certain features don&#39;t work without it, even though they could... there is no reason for openai to not use a background process

So I got bored of waiting, and decided to build my own in @pidotdev

But I realized, I could do much more than a simple cron job, with my recently upgraded osolmaz/pi-workflows extension

A cron job is a loop after all. Being a loop, I can represent it as a workflow graph

So I created a built in `monitor` workflow to mimic cron behavior. The agent is forced into a loop where it re-checks a very long-running job every 1 hour, and it is instructed to autonomously correct it and fix any bugs if any are encountered

The same functionality can be achieved by iamwrm/pi-unified-exec as well, which implements codex-like auto-forking exec behavior. But there is a chance the model messes up exec, or does not re-arm the next sleep() properly once one of them exits

My monitor workflow on the other hand is deterministic. I can make the agent loop infinitely, and there is nothing the agent can do to evade the task. I just ask the agent to monitor something, and it starts it automatically

This lets me just fire off week-long jobs, and forget about it! It even survives codex usage depletion, by auto-recovering once my quota resets

Oh also, @ratatui_rs is a delight! I created piw, a viewer for my ongoing pi workflows. I just type piw, and can see the current state, or play back the finished ones</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
        <category>tweet</category>
        <pubDate>Mon, 10 Aug 2026 15:57:53 +0000</pubDate>
        <link>https://solmaz.io/x/2086844493672980650/</link>
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      <item>
        <title>Apparently this went to HN front page briefly last night, so sharing it here as well</title>
        <description>Apparently this went to HN front page briefly last night, so sharing it here as well

YOLO safely with your agents 🤖

Give your GitHub/Hugging Face accounts + ability to run sudo safely to your agent. No need to create an agent account, or clickops policies on GitHub etc.

Give it merge access to repo X for 5 minutes, 30 minutes, 1 time, 100 times, anything...

Then give it unlimited access to repo Y forever. Other repos stay untouched. Complete flexibility that GitHub policies actually cannot give you due to the way that they are designed

You can get notifications through telegram, and can approve its requests

No need to pay $4 to GitHub if you simply want protection against force push. unYOLO blocks force pushes by default, unless you explicitly allow to

Your exfiltratable, internet-accessing agent/claw gets its own Linux/Mac account, and has to access these services through unYOLO

The video shows how it works!

Visit:</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
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        <pubDate>Mon, 10 Aug 2026 09:55:11 +0000</pubDate>
        <link>https://solmaz.io/x/2086753216981770308/</link>
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        <title>Local AI will favor LPDDR and MoE</title>
        <description>This.

LPDDR chips are cheaper to produce and run

GDDR/HBM will likely keep being more expensive

Most consumer GPUs will converge on a GB10 like form factor

As much as us hobbyists love to project this ideal of running a GPU cluster at home, most working people will prefer smaller form factors, and will not want to pay hundreds of $$$ in electricity bills every month

DGX Spark/GB10 runs at around 90-150 Watts
RTX Pro 6000 runs at 600 Watts FOR THE GPU ALONE, and can cost 3-5x more than GB10. Despite having 25% less memory capacity than GB10...

Looking at this, LPDDR will be orders of magnitude more commonplace at home

Architectures  will develop accordingly. Future local AI will be dominated by MoE and similar architectures which leverage mid-sized models with smaller number of active parameters

That is why Qwen3.x-35B-A3B is a more useful model on the Spark than Qwen3.x-27B, despite the latter being a better model. Same for Gemma

I can run A3B at 60 decode tok/s single session or 6x20 decode tok/s in parallel, whereas 27b only reaches 1/3rd of that

Future of local AI is DDR/LPDDR and MoE/adjacent architectures, for the average person</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
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        <pubDate>Mon, 10 Aug 2026 07:24:46 +0000</pubDate>
        <link>https://solmaz.io/x/2086715364642365804/</link>
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      <item>
        <title>urgh typo, I meant 35-40 tok/s</title>
        <description>urgh typo, I meant 35-40 tok/s</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
        <category>tweet</category>
        <pubDate>Sun, 09 Aug 2026 10:49:19 +0000</pubDate>
        <link>https://solmaz.io/x/2086404453222298076/</link>
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        <title>Apparently Alibaba did all this work, published a paper, but did not create a public backup of...</title>
        <description>Apparently Alibaba did all this work, published a paper, but did not create a public backup of repos used in the review tasks https://t.co/aoZoYD79Vh

Then keycloak and nodejs repos got force pushed, so the commits for 6 of the tasks got lost :(

I recovered 4 of them, but 2 commits are still missing:

keycloak/keycloak#35645
460f8008f86d3fa8f62da63e26d8bdc306af60b2

nodejs/node#56185
b2255442712cb6db83d112deb6ba61197d06a5f3

Would anyone happen to have them backed up locally or somewhere in a fork?</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
        <category>tweet</category>
        <pubDate>Sun, 09 Aug 2026 10:03:27 +0000</pubDate>
        <link>https://solmaz.io/x/2086392908463382897/</link>
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        <title>I have made an update to my theoretical upper bound calculation to also predict prefill speed</title>
        <description>I have made an update to my theoretical upper bound calculation to also predict prefill speed

Prefill relaxes the assumption we make for decode, that it is only be memory bottlenecked. So prefill can be both compute or memory bottlenecked. I use the FLOP limits reported by hardware producers for the estimates:

These estimates will also be available in https://t.co/SGQepULIW7 for indexed model and hardware in a couple days, once a long running job finishes

Blog post: https://t.co/cVy6Th57a1</description>
        <dc:creator>Onur Solmaz</dc:creator>
        <category>x</category>
        <category>tweet</category>
        <pubDate>Sun, 09 Aug 2026 06:57:19 +0000</pubDate>
        <link>https://solmaz.io/x/2086346067952677215/</link>
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