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    <dc:creator>pskomoroch</dc:creator><description><![CDATA[open sourcing Marlin-2B 
a tiny VLM to extract structured information from videos

Marlin is finetuned for two questions devs want to ask in their videos: what is happening, and when?

Best open model in its weight class, competitive with Gemini-2.5-flash at only 2B params ]]></description>
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    <title>(154) How to use LTX Director - A Free Open Source Tool for Creating LTX 2.3 AI Videos Locally in ComfyUI - YouTube</title>
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    <dc:creator>pskomoroch</dc:creator><description><![CDATA[https://ltx.io/model/model-blog/ltx-2...]]></description>
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    <title>nickwinder/synthteam: Consult distilled personas of colleagues — build them from Slack history, get one colleague's take, or convene a deliberating panel. A plugin for Claude Code and Codex.</title>
    <dc:date>2026-05-18T21:05:04+00:00</dc:date>
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    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Consult distilled personas of colleagues — build them from Slack history, get one colleague's take, or convene a deliberating panel. A plugin for Claude Code and Codex.

Resources
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 0 watching
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 4 forks
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1

claude Claude
Languages
JavaScript
100.0%]]></description>
<dc:subject>slack ai conversations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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    <dc:creator>pskomoroch</dc:creator><description><![CDATA[these logs act as a paper trail of my growth.
every decision i make ends up in a Decision Log, organised by project and date with the reasoning attached. when i'm about to make a similar call, Claude reads the log and tells me what past me already worked out.
everything i ship ends up in an Impact Log, sorted by quarter with the metric movement and the context. when i want to see the arc of what i've shipped in the past, it's all there and retrievable.
]]></description>
<dc:subject>ai knowledge claude obsidian wiki</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:f70116dff036/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claude"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:obsidian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:wiki"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://shop.tetheria.ai/">
    <title>TetherIA</title>
    <dc:date>2026-05-08T03:01:25+00:00</dc:date>
    <link>https://shop.tetheria.ai/</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Assembled tendon driven robotic hand for $720]]></description>
<dc:subject>robotics hand tendons opensource</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:0157c1f9d5e0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:hand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:tendons"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:opensource"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/mebrown47/CUDA_spectrum">
    <title>mebrown47/CUDA_spectrum: A simple CUDA FFT spectrum tool</title>
    <dc:date>2026-05-03T01:49:50+00:00</dc:date>
    <link>https://github.com/mebrown47/CUDA_spectrum</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>sdr spectrum analyzer cuda</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:95ed6eb86187/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:sdr"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:spectrum"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:analyzer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:cuda"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://huggingface.co/datasets/clem/ml-intern-sessions/blob/main/sessions/2026-05-01/5eb4110b-756c-428a-88e5-17baef6074a7.jsonl">
    <title>sessions/2026-05-01/5eb4110b-756c-428a-88e5-17baef6074a7.jsonl · clem/ml-intern-sessions at main</title>
    <dc:date>2026-05-03T01:49:22+00:00</dc:date>
    <link>https://huggingface.co/datasets/clem/ml-intern-sessions/blob/main/sessions/2026-05-01/5eb4110b-756c-428a-88e5-17baef6074a7.jsonl</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>reachy mini robotics ai huggingface</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:cb613d1380dd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:reachy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:mini"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:huggingface"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://openai.com/index/where-the-goblins-came-from/">
    <title>Where the goblins came from | OpenAI</title>
    <dc:date>2026-04-30T04:32:06+00:00</dc:date>
    <link>https://openai.com/index/where-the-goblins-came-from/</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Why it matters
Depending on who you ask, the goblins are a delightful or annoying quirk of the model. But they are also a powerful example of how reward signals can shape model behavior in unexpected ways, and how models can learn to generalize rewards in certain situations to unrelated ones. Taking the time to understand why a model is behaving in a strange way, and building out ways to investigate those patterns quickly, is an important capability for our research team. This investigation resulted in new tools for the research team to audit model behavior and fix behavior problems at their root.

]]></description>
<dc:subject>goblins ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:065706887611/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:goblins"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/espressif/esp-drone">
    <title>espressif/esp-drone: Mini Drone/Quadcopter Firmware for ESP32 and ESP32-S Series SoCs.</title>
    <dc:date>2026-04-27T22:44:48+00:00</dc:date>
    <link>https://github.com/espressif/esp-drone</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[ESP-Drone is an open source solution based on Espressif ESP32/ESP32-S2/ESP32-S3 Wi-Fi chip, which can be controlled by a mobile APP or gamepad over Wi-Fi connection. ESP-Drone comes with simple hardware, clear and extensible code architecture, and therefore this project can be used in STEAM education and other fields. The main code is ported from Crazyflie open source project with GPL3.0 protocol.]]></description>
<dc:subject>drone esp32</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:7ef4136b67f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:drone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:esp32"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/marshallrichards/turbodrone">
    <title>marshallrichards/turbodrone: reverse engineering the best-selling drones on Amazon to control programmatically</title>
    <dc:date>2026-04-20T22:37:13+00:00</dc:date>
    <link>https://github.com/marshallrichards/turbodrone</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[reverse engineering the best-selling drones on Amazon to control programmatically]]></description>
<dc:subject>drones api ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:f5d2c6ba9f95/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:drones"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:api"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://dev.to/ankk98/humanoid-compute-price-vs-performance-842">
    <title>Humanoid Compute: Price vs. Performance - DEV Community</title>
    <dc:date>2026-04-18T09:45:56+00:00</dc:date>
    <link>https://dev.to/ankk98/humanoid-compute-price-vs-performance-842</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai jetson robotics vla yolo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:ea9883f3b96f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:jetson"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:vla"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:yolo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/chrysb/status/2043020014035570784?s=12">
    <title>(8) Chrys Bader on X: &quot;Why long-term memory for LLMs remains unsolved&quot; / X</title>
    <dc:date>2026-04-13T11:51:49+00:00</dc:date>
    <link>https://x.com/chrysb/status/2043020014035570784?s=12</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Despite what you see, long-term memory for conversational LLMs remains an unsolved problem.
The dream is: the model remembers what you said before and draws meaning across it over time. Not just recall, but interpretation, narrative, the kind of memory that makes a conversation feel continuous and cumulative across months or years.
Today, you can achieve an illusion of this dream. For days, or weeks if you're lucky. Until the LLM starts forgetting and the illusion breaks.]]></description>
<dc:subject>memory ai llm agents</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:ee3675765e44/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:agents"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://foodtruckbench.com/blog/qwen-3-6-plus">
    <title>Qwen 3.6 Plus Benchmark Results: First Chinese Model To Survive 5/5 | FoodTruck Bench | FoodTruck Bench</title>
    <dc:date>2026-04-13T06:03:17+00:00</dc:date>
    <link>https://foodtruckbench.com/blog/qwen-3-6-plus</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:d7b9693b0eb6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/pdf/2602.04101">
    <title>[untitled]</title>
    <dc:date>2026-04-12T23:13:20+00:00</dc:date>
    <link>https://arxiv.org/pdf/2602.04101</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai llm fine-tuning small-models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:3544a4e84a27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:fine-tuning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:small-models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.lesswrong.com/posts/jTGbKKGqs5EdyYoRc/most-people-can-t-juggle-one-ball">
    <title>Most people can't juggle one ball — LessWrong</title>
    <dc:date>2026-04-12T22:28:51+00:00</dc:date>
    <link>https://www.lesswrong.com/posts/jTGbKKGqs5EdyYoRc/most-people-can-t-juggle-one-ball</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Mill's Mess, ]]></description>
<dc:subject>juggling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:a263d01315f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:juggling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://juggle.fandom.com/wiki/Mills_mess">
    <title>Mills mess | Juggle Wiki | Fandom</title>
    <dc:date>2026-04-12T22:28:43+00:00</dc:date>
    <link>https://juggle.fandom.com/wiki/Mills_mess</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>juggling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:0fcc7fb6fd7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:juggling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/watch?v=8jBX3RatIus">
    <title>(123) Hermes Agent Local Ai Setup Guide with Qwen3.5 + OpenWebUI - YouTube</title>
    <dc:date>2026-04-09T09:27:53+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=8jBX3RatIus</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Hermes Agent is a great harness for Local Ai models. I take you through the setup, running with vLLM and integration with OpenwebUI. This takes my Proxmox homelab to another level.
]]></description>
<dc:subject>ai hermes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:3e5fa280a188/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:hermes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.google.com/search?q=240v+wiring+upgrade&amp;oq=240v+wiring+upgrade&amp;gs_lcrp=EgZjaHJvbWUyCQgAEEUYORigATIHCAEQIRigATIHCAIQIRigATIHCAMQIRiPAtIBCDU1NzZqMGo3qAIAsAIA&amp;sourceid=chrome&amp;ie=UTF-8#fpstate=ive&amp;vld=cid:bef65dee,vid:agwiVBXS3kw,st:214">
    <title>240v wiring upgrade - Google Search</title>
    <dc:date>2026-04-05T10:18:28+00:00</dc:date>
    <link>https://www.google.com/search?q=240v+wiring+upgrade&amp;oq=240v+wiring+upgrade&amp;gs_lcrp=EgZjaHJvbWUyCQgAEEUYORigATIHCAEQIRigATIHCAIQIRigATIHCAMQIRiPAtIBCDU1NzZqMGo3qAIAsAIA&amp;sourceid=chrome&amp;ie=UTF-8#fpstate=ive&amp;vld=cid:bef65dee,vid:agwiVBXS3kw,st:214</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[How to Wire for 240 Volts in the USA | CircuitBread Practicals]]></description>
<dc:subject>wiring 240V</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:71da4cf0a0d3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:wiring"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:240V"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/hnshah/screenmuse/">
    <title>hnshah/screenmuse: The most advanced macOS screen recorder, screencast, and screenshot tool. Native Swift + ScreenCaptureKit + on-device AI.</title>
    <dc:date>2026-03-27T05:29:56+00:00</dc:date>
    <link>https://github.com/hnshah/screenmuse/</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[The most advanced macOS screen recorder, screencast, and screenshot tool. Native Swift + ScreenCaptureKit + on-device AI.]]></description>
<dc:subject>ai screenrecorder hiten_shah</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:b3781ba8d50b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:screenrecorder"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:hiten_shah"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/GuptaRK22/status/2036884921969762402">
    <title>(1) Ravi Gupta on X: &quot;The guy who rebuilt Google Maps in a weekend without ai is showing you what’s possible now with ai&quot; / X</title>
    <dc:date>2026-03-25T22:26:41+00:00</dc:date>
    <link>https://x.com/GuptaRK22/status/2036884921969762402</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:d6603fe62abe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/danveloper/status/2034353876753592372">
    <title>Dan Woods on X: &quot;Autoresearching Apple's &quot;LLM in a Flash&quot; to run Qwen 397B locally&quot; / X</title>
    <dc:date>2026-03-24T08:25:32+00:00</dc:date>
    <link>https://x.com/danveloper/status/2034353876753592372</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[This is something I've been waiting to become possible ever since I read Apple's "LLM in a Flash" paper three years ago. The paper laid out a clear thesis for running models that exceed DRAM capacity by streaming weights from flash storage, and Apple had been shipping hardware that seemed purpose-built for exactly this kind of workload. I've been waiting and waiting for Apple to do something with it, and they just haven't. Running really big models locally has been a bit of a white whale for me, because local AI is incredibly compelling but smaller models just aren't very smart, and they still push GPU and memory pretty hard on their own. What I wanted was frontier-class intelligence running on hardware I own, and I've had this assumption for a while now that Apple's architecture should make it possible.]]></description>
<dc:subject>llm ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:0d9d76260d8d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/Voxyz_ai/status/2035093224117666076">
    <title>Vox on X: &quot;best coding agent skill i've used. just tried garry tan's gstack today, three things stood out: /office-hours didn't ask me 6 questions and stop. it kept going. challenged my framing, told me i was solving the wrong problem, generated 3 impleme</title>
    <dc:date>2026-03-21T10:41:53+00:00</dc:date>
    <link>https://x.com/Voxyz_ai/status/2035093224117666076</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[best coding agent skill i've used. just tried garry tan's gstack today, three things stood out:

/office-hours didn't ask me 6 questions and stop. it kept going. challenged my framing, told me i was solving the wrong problem, generated 3 implementation approaches with effort estimates, then wrote a design doc that every other skill in the system reads automatically.

/qa opened a real browser, clicked through my actual UI, found a bug, fixed it, wrote a regression test, and verified the fix. the agent has eyes now.

the whole thing is one sprint pipeline. /office-hours feeds into /plan-ceo-review feeds into /plan-eng-review feeds into /review feeds into /qa feeds into /ship. nothing falls through because every step reads what came before it.

most people will bookmark this. almost nobody will install it.]]></description>
<dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:52029eec4829/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/doodlestein/status/2035233207965122943">
    <title>Jeffrey Emanuel on X: &quot;This is truly the most alpha I can give people. If you can get this loop going for yourself and your use case, it doesn’t take many iterations before you can start doing really extraordinary things.&quot; / X</title>
    <dc:date>2026-03-21T09:39:03+00:00</dc:date>
    <link>https://x.com/doodlestein/status/2035233207965122943</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[This is truly the most alpha I can give people. If you can get this loop going for yourself and your use case, it doesn’t take many iterations before you can start doing really extraordinary things.]]></description>
<dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:755750a83fd2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/watch?v=mmbkP8NARH4">
    <title>(103) Get Started with Unsloth Studio: Generate Data &amp; Fine-Tune LLMs Locally on any NVIDIA GPU - YouTube</title>
    <dc:date>2026-03-20T12:48:19+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=mmbkP8NARH4</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai llm finetuning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:ad5149d908ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:finetuning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.reddit.com/r/Qwen_AI/comments/1ryoaub/macbook_m5_pro_qwen35_fully_local_ai_security/">
    <title>MacBook M5 Pro + Qwen3.5 = Fully Local AI Security System — 93.8% Accuracy, 25 tok/s, No Cloud Needed (96-Test Benchmark vs GPT-5.4) : r/Qwen_AI</title>
    <dc:date>2026-03-20T11:47:08+00:00</dc:date>
    <link>https://www.reddit.com/r/Qwen_AI/comments/1ryoaub/macbook_m5_pro_qwen35_fully_local_ai_security/</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[TL;DR: The M5 Pro just dropped, so here's a real AI workload instead of another Geekbench score. We run Qwen3.5 as the brain of a fully local home security system and benchmarked it against OpenAI cloud models on a custom 96-test suite. The Qwen3.5-9B scores 93.8% — within 4 points of GPT-5.4 — while running entirely on the M5 Pro at 25 tok/s, 765ms TTFT, using only 13.8 GB of unified memory. The 35B MoE variant hits 42 tok/s with a 435ms TTFT — faster first-token than any OpenAI cloud endpoint we tested. Zero API costs, full data privacy, all local. Full results: https://www.sharpai.org/benchmark/

What is HomeSec-Bench?
HomeSec-Bench is a benchmark we created to evaluate LLMs on real home security assistant workflows — not generic chat, but the actual reasoning, triage, and tool use an AI home security system needs:]]></description>
<dc:subject>ai home_security</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:2f2e088aabb0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:home_security"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/daniel_mac8/status/2032282196673708042">
    <title>(1) Dan McAteer on X: &quot;Manus ex-backend lead had a genius insight text based clis beat structured tool calling for ai agents all day because unix commands appear in training data going back to the 1970s text is the native language of the command line AND </title>
    <dc:date>2026-03-13T06:05:13+00:00</dc:date>
    <link>https://x.com/daniel_mac8/status/2032282196673708042</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Manus ex-backend lead had a genius insight

text based clis beat structured tool calling for ai agents all day because unix commands appear in training data going back to the 1970s

text is the native language of the command line

AND

text is the native language of llms]]></description>
<dc:subject>llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:14dce0c1e59e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/kchonyc/status/2032229823519568145">
    <title>(5) Kyunghyun Cho on X: &quot;thanks to @karpathy , now i have cracked the mystery why my agent doesn't follow my instruction closely enough. https://t.co/WrMWU32h3I&quot; / X</title>
    <dc:date>2026-03-13T03:57:59+00:00</dc:date>
    <link>https://x.com/kchonyc/status/2032229823519568145</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[thanks to 
@karpathy
 , now i have cracked the mystery why my agent doesn't follow my instruction closely enough.]]></description>
<dc:subject>llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:ce7bea8d1e9e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/const_reborn/status/2032002840961442279">
    <title>(2) const on X: &quot;I have 6 agents running now, two are doing ML research, one is cloning Claude code, two trade hyperliquid and one mines Bittensor. All they need is compute from Bittensor (Lium, Basilica and Targon) and inference from Chutes and Openroute</title>
    <dc:date>2026-03-12T12:02:56+00:00</dc:date>
    <link>https://x.com/const_reborn/status/2032002840961442279</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[I have 6 agents running now, two are doing ML research, one is cloning Claude code, two trade hyperliquid and one mines Bittensor. 

All they need is compute from Bittensor (Lium,  Basilica and Targon) and inference from Chutes and Openrouter.

The trading agents pay for the compute and inference by buying credits with proceeds. 

I could literally die and this system wouldn’t turn off. Indeed it’s programmed to just get better.
Last edited
12:56 AM · Mar 12, 2026
·
41.1K
 Views]]></description>
<dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:9b08152603a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/msitarzewski/agency-agents">
    <title>GitHub - msitarzewski/agency-agents: A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven delivera</title>
    <dc:date>2026-03-12T11:33:39+00:00</dc:date>
    <link>https://github.com/msitarzewski/agency-agents</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>agent claude llm agents</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:5ca3bfec9003/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:agent"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claude"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:agents"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blog.roboflow.com/zero-shot-pose-estimation-for-robotics/">
    <title>Zero-Shot Pose Estimation for Robotics with Roboflow</title>
    <dc:date>2026-03-11T18:58:59+00:00</dc:date>
    <link>https://blog.roboflow.com/zero-shot-pose-estimation-for-robotics/</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Roboflow Workflows is a low-code, web-based platform that lets you visually build computer vision pipelines by connecting reusable blocks.

It includes pre-built blocks for tasks such as detection, classification, segmentation, tracking (e.g., ByteTrack), and integration with large models or business logic, which can be chained together to create end-to-end computer vision pipelines with little to no coding.

Here are the key reasons to use Roboflow Workflows for zero-shot pose estimation:

Ready-to-Use Pose Estimation Models: Roboflow provides pre-deployed zero-shot pose estimation models in multiple size variants, allowing you to start immediately without any installation.
Optimized Real-Time Edge Deployment: Supports local deployment on edge devices with optimized inference servers that run with minimal code, working within hardware constraints and ensuring low-latency performance for robotic control and human-robot interaction.
Provides Built-In Tracking Blocks: Includes pre-implemented tracking algorithms, such as ByteTrack, as workflow blocks to ensure consistent keypoint tracking in multi-person or occluded scenes.
Accelerates Experimentation and Iteration: Offers a visual workflow builder, dataset versioning, and quick redeployment to speed up robotics R&D and production cycles.
Simplifies Model Training and Iteration: Makes dataset collection and training easier with automatic augmentation, dataset versioning, fine-tuning, and active learning blocks, enabling models to improve quickly.
Simplifies Robotics System Integration: Supports RTSP streams, local cameras, and APIs, enabling seamless integration with robotics control pipelines.
]]></description>
<dc:subject>ai computervision pose-estimation roboflow</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:4f17161fb036/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:computervision"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:pose-estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:roboflow"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/NickPittas/DirectorsConsole">
    <title>GitHub - NickPittas/DirectorsConsole: A web application for prompt generation and multiple ComfyUI remote and local connections for image and video generation in parallel in an infinite canvas · GitHub</title>
    <dc:date>2026-03-08T02:06:10+00:00</dc:date>
    <link>https://github.com/NickPittas/DirectorsConsole</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[A web application for prompt generation and multiple ComfyUI remote and local connections for image and video generation in parallel in an infinite canvas

Pretty cool unified AI VFX production pipeline.
- Cinema Prompt Engineering with ~110 film and animation presets; 
- an infinite Storyboard Canvas + distributed rendering across multiple ComfyUI nodes; 
- 13+ LLM providers.]]></description>
<dc:subject>ai video</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:aedf286b5edf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:video"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/toddsaunders/status/2029301170670309740">
    <title>Todd Saunders on X: &quot;Fun command built in Claude Code: /cost-estimate It scans your codebase and cross-references current market rates to calculate what your project would've cost a real team to build. It looks at all the APIs, integrations, everything. W</title>
    <dc:date>2026-03-05T09:34:00+00:00</dc:date>
    <link>https://x.com/toddsaunders/status/2029301170670309740</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Fun command built in Claude Code: /cost-estimate

It scans your codebase and cross-references current market rates to calculate what your project would've cost a real team to build.

It looks at all the APIs, integrations, everything. 

Without AI: ~2.8 years. ~$650k.

With AI: 30 hours.

It's absurd when you start to think about it like this.]]></description>
<dc:subject>ai claude claudecode</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:01055c6dc7be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claude"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claudecode"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.reddit.com/r/LocalLLaMA/comments/1rj21zm/open_source_tool_for_finetuningevals_now_works/">
    <title>Open source tool for fine-tuning/evals now works with NVIDIA DGX Spark (if your lab has one) : r/LocalLLaMA</title>
    <dc:date>2026-03-03T13:08:58+00:00</dc:date>
    <link>https://www.reddit.com/r/LocalLLaMA/comments/1rj21zm/open_source_tool_for_finetuningevals_now_works/</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:45b42baa9cdf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://dmux.ai/">
    <title>dmux - Parallel agents with tmux and worktrees</title>
    <dc:date>2026-03-03T11:21:26+00:00</dc:date>
    <link>https://dmux.ai/</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Parallel agents with tmux and worktrees

Manage multiple AI coding agents in isolated git worktrees. Branch, develop, and merge — all in parallel.]]></description>
<dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:dc5f4f06511c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/AlexsJones/llmfit">
    <title>GitHub - AlexsJones/llmfit: 206 models. 30 providers. One command to find what runs on your hardware.</title>
    <dc:date>2026-02-25T07:02:25+00:00</dc:date>
    <link>https://github.com/AlexsJones/llmfit</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[206 models. 57 providers. One command to find what runs on your hardware.

A terminal tool that right-sizes LLM models to your system's RAM, CPU, and GPU. Detects your hardware, scores each model across quality, speed, fit, and context dimensions, and tells you which ones will actually run well on your machine.

Ships with an interactive TUI (default) and a classic CLI mode. Supports multi-GPU setups, MoE architectures, dynamic quantization selection, and speed estimation.

]]></description>
<dc:subject>ai tools llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:adf6f6ed4f7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/elvissun/status/2025920521871716562">
    <title>Elvis on X: &quot;OpenClaw + Codex/ClaudeCode Agent Swarm: The One-Person Dev Team [Full Setup]&quot; / X</title>
    <dc:date>2026-02-24T11:07:15+00:00</dc:date>
    <link>https://x.com/elvissun/status/2025920521871716562</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[I don't use Codex or Claude Code directly anymore.
I use OpenClaw as my orchestration layer. My orchestrator, Zoe, spawns the agents, writes their prompts, picks the right model for each task, monitors progress, and pings me on Telegram when PRs are ready to merge.
Proof points from the last 4 weeks:
- 94 commits in one day. My most productive day - I had 3 client calls and didn't open my editor once. The average is around 50 commits a day. 
- 7 PRs in 30 minutes. Idea to production are blazing fast because coding and validations are mostly automated.
- Commits → MRR: I use this for a real B2B SaaS I'm building — bundling it with founder-led sales to deliver most feature requests same-day. Speed converts leads into paying customers.
]]></description>
<dc:subject>ai llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:795efc5293fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/SymoneBeez/status/2025614490524332077">
    <title>(1) Symoné B. Beez on X: &quot;Tech careers are now split into 3 tracks https://t.co/xDNrahcH7G&quot; / X</title>
    <dc:date>2026-02-23T02:48:15+00:00</dc:date>
    <link>https://x.com/SymoneBeez/status/2025614490524332077</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Tech careers are now split into 3 tracks
]]></description>
<dc:subject>ai jobs</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:72387614f468/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:jobs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://wokwi.com/projects/301404853501952521">
    <title>pico-pio-7segment.ino - Wokwi ESP32, STM32, Arduino Simulator</title>
    <dc:date>2026-02-21T09:51:58+00:00</dc:date>
    <link>https://wokwi.com/projects/301404853501952521</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>circuit simulator</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:03cda3f6ca42/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:circuit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:simulator"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/RobertHaisfield/status/2022449249326420137">
    <title>Rob Haisfield on X: &quot;Why is nobody talking about the fact that both Gemini and Kimi K2.5 can record video runs of scripts with Puppeteer, WATCH THEM through multimodal video input, and reflect on how the code leads to what they see? Has anyone set up the </title>
    <dc:date>2026-02-14T05:07:43+00:00</dc:date>
    <link>https://x.com/RobertHaisfield/status/2022449249326420137</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>llm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:31996f12c380/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://developers.openai.com/blog/skills-shell-tips">
    <title>Shell + Skills + Compaction: Tips for long-running agents that do real work</title>
    <dc:date>2026-02-12T02:19:22+00:00</dc:date>
    <link>https://developers.openai.com/blog/skills-shell-tips</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Practical patterns for building with skills, hosted shell, and server-side compaction in the Responses API.]]></description>
<dc:subject>openai agents shell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:26e2200a1eea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:openai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:agents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:shell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/thatguybg/status/2018492406186836232">
    <title>brett goldstein on X: &quot;maybe the most useful part of my claude code work flow is this line &quot;research the industry-standard approach to this problem use it to guide yours&quot;&quot; / X</title>
    <dc:date>2026-02-03T14:20:30+00:00</dc:date>
    <link>https://x.com/thatguybg/status/2018492406186836232</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[
brett goldstein
@thatguybg
maybe the most useful part of my claude code work flow is this line

"research the industry-standard approach to this problem use it to guide yours"]]></description>
<dc:subject>claude claudecode ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:ac391f8028f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claude"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claudecode"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blog.natsuki-cloud.dev/posts/deskpi-homelab-architecture/">
    <title>Small But Mighty Homelab: DeskPi 12U Running 20+ Services | Natsuki's Tech Blog</title>
    <dc:date>2026-02-02T03:39:27+00:00</dc:date>
    <link>https://blog.natsuki-cloud.dev/posts/deskpi-homelab-architecture/</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[You don’t need a server room to run a powerful homelab. With just a few host machines - a couple of Raspberry Pis, a mini PC, and a Jetson - you can run 20+ services: media streaming, photo backup, password management, smart home automation, monitoring, and even a local AI voice assistant. All in a compact, elegant setup that fits in a corner of a tiny apartment.

This post walks through my homelab architecture - how I got here, how it’s organized, and what it can do.]]></description>
<dc:subject>homelab ops ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:8233bbd61c44/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:homelab"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ops"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/akothari/status/2017663493819011271">
    <title>Akshay Kothari on X: &quot;don't think market has yet priced in kimi k2.5&quot; / X</title>
    <dc:date>2026-02-01T16:07:17+00:00</dc:date>
    <link>https://x.com/akothari/status/2017663493819011271</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:e9e1e50373f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/hailo-ai/hailo-apps">
    <title>hailo-ai/hailo-apps</title>
    <dc:date>2026-01-31T07:31:07+00:00</dc:date>
    <link>https://github.com/hailo-ai/hailo-apps</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai raspberrypi computervision</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:820d8b48af1f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:raspberrypi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:computervision"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://unsloth.ai/docs/basics/claude-codex">
    <title>How to Run Local LLMs with Claude Code &amp; OpenAI Codex | Unsloth Documentation</title>
    <dc:date>2026-01-29T23:21:36+00:00</dc:date>
    <link>https://unsloth.ai/docs/basics/claude-codex</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Run Claude Code and OpenAI Codex on your local device guide.

This step-by-step guide shows you how to connect open LLMs to Claude Code and Codex entirely locally, complete with screenshots. Run using any open model like DeepSeek, Qwen and Gemma.

For this tutorial, we’ll use GLM-4.7-Flash, the strongest 30B MoE agentic & coding model as of Jan 2026 to autonomously fine-tune an LLM with Unsloth. You can swap in any other model, just update the model names in your scripts.]]></description>
<dc:subject>claudecode local llm finetuning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:767537cd8817/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:finetuning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://research.google/blog/sensorlm-learning-the-language-of-wearable-sensors/">
    <title>SensorLM: Learning the language of wearable sensors</title>
    <dc:date>2026-01-19T22:54:50+00:00</dc:date>
    <link>https://research.google/blog/sensorlm-learning-the-language-of-wearable-sensors/</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai sensors wearables</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:1caf0682e55f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:sensors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:wearables"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.notboring.co/p/robot-steps">
    <title>Many Small Steps for Robots, One Giant Leap for Mankind</title>
    <dc:date>2026-01-18T22:56:16+00:00</dc:date>
    <link>https://www.notboring.co/p/robot-steps</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>robotics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:535b0ce014ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:robotics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/affaan-m/everything-claude-code">
    <title>affaan-m/everything-claude-code: Complete Claude Code configuration collection - agents, skills, hooks, commands, rules, MCPs. Battle-tested configs from an Anthropic hackathon winner.</title>
    <dc:date>2026-01-18T07:02:52+00:00</dc:date>
    <link>https://github.com/affaan-m/everything-claude-code</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Complete Claude Code configuration collection - agents, skills, hooks, commands, rules, MCPs. Battle-tested configs from an Anthropic hackathon winner.]]></description>
<dc:subject>claudecode claude</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:098d7932e38e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claudecode"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claude"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/blader/claude-code-continuous-learning-skill">
    <title>blader/claude-code-continuous-learning-skill: A Claude Code skill for autonomous skill extraction and continuous learning. Have Claude Code get smarter as it works.</title>
    <dc:date>2026-01-18T04:24:36+00:00</dc:date>
    <link>https://github.com/blader/claude-code-continuous-learning-skill</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[used claude code to make a little claude code skill that learns new claude code skills as you use claude code

Every time you use an AI coding agent, it starts from zero. You spend an hour debugging some obscure error, the agent figures it out, session ends. Next time you hit the same issue? Another hour.

This skill fixes that. When Claude Code discovers something non-obvious (a debugging technique, a workaround, some project-specific pattern), it saves that knowledge as a new skill. Next time a similar problem comes up, the skill gets loaded automatically.

]]></description>
<dc:subject>claude ai llm claudecode skills</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:44d7f181a2c6/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claudecode"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:skills"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://brianlovin.com/writing/give-your-agent-a-stopwatch">
    <title>Give your agent a stopwatch</title>
    <dc:date>2026-01-16T23:33:20+00:00</dc:date>
    <link>https://brianlovin.com/writing/give-your-agent-a-stopwatch</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>agents bots ai feedback</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:70b6620d6200/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:agents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:bots"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:feedback"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/tobi/qmd">
    <title>tobi/qmd: mini cli search engine for your docs, knowledge bases, meeting notes, whatever. Tracking current sota approaches while being all local</title>
    <dc:date>2026-01-11T13:06:22+00:00</dc:date>
    <link>https://github.com/tobi/qmd</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[An on-device search engine for everything you need to remember. Index your markdown notes, meeting transcripts, documentation, and knowledge bases. Search with keywords or natural language. Ideal for your agentic flows.

QMD combines BM25 full-text search, vector semantic search, and LLM re-ranking—all running locally via node-llama-cpp with GGUF models.]]></description>
<dc:subject>search llm ai local</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:2c6bb969d01b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:llm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:local"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/gfodor/status/2010102563497218427">
    <title>gfodor.id on X: &quot;So we indisputably have everything needed to make a v1 Jarvis. I am not using one. Is anyone? Like what is going on here&quot; / X</title>
    <dc:date>2026-01-11T12:13:37+00:00</dc:date>
    <link>https://x.com/gfodor/status/2010102563497218427</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:20491d778f79/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/snarktank/ralph">
    <title>snarktank/ralph: Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete.</title>
    <dc:date>2026-01-11T11:57:14+00:00</dc:date>
    <link>https://github.com/snarktank/ralph</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete.]]></description>
<dc:subject>ralph ralphwiggum claudecode</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:d5d810ee3110/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ralph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ralphwiggum"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claudecode"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://lochie.dev/posts/ralph-sqlite-ui/">
    <title>Ralph Experiment - SQLite UI – Lochie.dev</title>
    <dc:date>2026-01-11T11:56:37+00:00</dc:date>
    <link>https://lochie.dev/posts/ralph-sqlite-ui/</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>ralph ralphwiggum claudecode</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:c120e727a7c4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ralph"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claudecode"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/Seeed-Projects/jetson-examples">
    <title>Seeed-Projects/jetson-examples: The jetson-examples repository by Seeed Studio offers a seamless, one-line command deployment to run vision AI and Generative AI models on the NVIDIA Jetson platform.</title>
    <dc:date>2026-01-11T11:55:51+00:00</dc:date>
    <link>https://github.com/Seeed-Projects/jetson-examples</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>nvidia jetson orin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:320a252cc268/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:nvidia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:jetson"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:orin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://wiki.seeedstudio.com/run_vlm_on_recomputer/">
    <title>How to Run VLM on reComputer | Seeed Studio Wiki</title>
    <dc:date>2026-01-11T11:54:46+00:00</dc:date>
    <link>https://wiki.seeedstudio.com/run_vlm_on_recomputer/</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>nvidia jetson orin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:495bcb4ed611/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:nvidia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:jetson"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:orin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://awesomeclaude.ai/ralph-wiggum">
    <title>Ralph Wiggum - AI Loop Technique for Claude Code - Awesome Claude</title>
    <dc:date>2026-01-10T08:53:42+00:00</dc:date>
    <link>https://awesomeclaude.ai/ralph-wiggum</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>claude claudecode vibecoding ai ralph ralphwiggum</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:09d6e218b3a3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:claude"/>
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</item>
<item rdf:about="https://www.aihero.dev/getting-started-with-ralph">
    <title>Getting Started With Ralph</title>
    <dc:date>2026-01-10T07:56:18+00:00</dc:date>
    <link>https://www.aihero.dev/getting-started-with-ralph</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Linear]]></description>
<dc:subject>ralph claudecode vibecoding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:666dea262a43/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:ralph"/>
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</item>
<item rdf:about="https://www.linkedin.com/posts/petesoder_duckdb-crunched-1tb-of-parquet-on-a-single-activity-7415439540313579520-MC_F/?rcm=ACoAAACxqGQB0tsroeHVUkkdsYf1fU2UbTI8eHo">
    <title>(28) Post | LinkedIn</title>
    <dc:date>2026-01-10T03:18:33+00:00</dc:date>
    <link>https://www.linkedin.com/posts/petesoder_duckdb-crunched-1tb-of-parquet-on-a-single-activity-7415439540313579520-MC_F/?rcm=ACoAAACxqGQB0tsroeHVUkkdsYf1fU2UbTI8eHo</link>
    <dc:creator>pskomoroch</dc:creator><dc:subject>streaming duckdb parquet</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:e50aa746c1b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:streaming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:duckdb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:parquet"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://x.com/AlexFinn/status/2009760244533178872">
    <title>Alex Finn on X: &quot;Every app I vibe code has the tech stack below Easy for beginners and free to start If you've never built an app before, just paste this list into Claude Code and you'll be good to go: Web framework: NextJS Hosting: Vercel Database: Supab</title>
    <dc:date>2026-01-09T23:24:01+00:00</dc:date>
    <link>https://x.com/AlexFinn/status/2009760244533178872</link>
    <dc:creator>pskomoroch</dc:creator><description><![CDATA[Web framework: NextJS
Hosting: Vercel
Database: Supabase
Auth: Clerk
Payments: Stripe
Styling: Tailwind
AI: logic- OpenAI, creativity- Claude, cheap tasks- Gemini Flash 3
Emails: Resend
AI I use to build it all: Claude Code
]]></description>
<dc:subject>saas ai stack vibecoding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:pskomoroch/b:056240a1e852/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:pskomoroch/t:vibecoding"/>
</rdf:Bag></taxo:topics>
</item>
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