#local-ai — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #local-ai, aggregated by home.social.
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The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
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The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
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I could not resist grabbing this sucker for $400 bucks. I feel like I stole it. So I guess with 80 PCIe 3 lanes (4 full length slots) and I can stuff 512GB of DDR3 in (16 DIMM slots!), I could load 4x GPUs in there.
So, which ones? Me thinks 4x Tesla P100 16GB GPUs are being bidded on now.
-2x Intel Xeon E5-2620 V2 Processor
-64 GB DDR3 (I will make it 128 methinks)
-ASUS Motherboard: Z9PE-D16
-1100w Power Supply
-ASUS Blu-Ray MDISC BurnerMight end up selling my 2080 ti LLM server if this can crank out serious tokens per second with a big arse LLM
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I could not resist grabbing this sucker for $400 bucks. I feel like I stole it. So I guess with 80 PCIe 3 lanes (4 full length slots) and I can stuff 512GB of DDR3 in (16 DIMM slots!), I could load 4x GPUs in there.
So, which ones? Me thinks 4x Tesla P100 16GB GPUs are being bidded on now.
-2x Intel Xeon E5-2620 V2 Processor
-64 GB DDR3 (I will make it 128 methinks)
-ASUS Motherboard: Z9PE-D16
-1100w Power Supply
-ASUS Blu-Ray MDISC BurnerMight end up selling my 2080 ti LLM server if this can crank out serious tokens per second with a big arse LLM
-
I could not resist grabbing this sucker for $400 bucks. I feel like I stole it. So I guess with 80 PCIe 3 lanes (4 full length slots) and I can stuff 512GB of DDR3 in (16 DIMM slots!), I could load 4x GPUs in there.
So, which ones? Me thinks 4x Tesla P100 16GB GPUs are being bidded on now.
-2x Intel Xeon E5-2620 V2 Processor
-64 GB DDR3 (I will make it 128 methinks)
-ASUS Motherboard: Z9PE-D16
-1100w Power Supply
-ASUS Blu-Ray MDISC BurnerMight end up selling my 2080 ti LLM server if this can crank out serious tokens per second with a big arse LLM
-
I could not resist grabbing this sucker for $400 bucks. I feel like I stole it. So I guess with 80 PCIe 3 lanes (4 full length slots) and I can stuff 512GB of DDR3 in (16 DIMM slots!), I could load 4x GPUs in there.
So, which ones? Me thinks 4x Tesla P100 16GB GPUs are being bidded on now.
-2x Intel Xeon E5-2620 V2 Processor
-64 GB DDR3 (I will make it 128 methinks)
-ASUS Motherboard: Z9PE-D16
-1100w Power Supply
-ASUS Blu-Ray MDISC BurnerMight end up selling my 2080 ti LLM server if this can crank out serious tokens per second with a big arse LLM
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Small update: Run LLMs Locally
Added Maple-Preview 20B-A1B using llama.cpp (6 GB, fastest 20B model).
Added Qwen 3.8 27B configuration for coding agents.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #clef
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Small update: Run LLMs Locally
Added Maple-Preview 20B-A1B using llama.cpp (6 GB, fastest 20B model).
Added Qwen 3.8 27B configuration for coding agents.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #clef
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Small update: Run LLMs Locally
Added Maple-Preview 20B-A1B using llama.cpp (6 GB, fastest 20B model).
Added Qwen 3.8 27B configuration for coding agents.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #clef
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Small update: Run LLMs Locally
Added Maple-Preview 20B-A1B using llama.cpp (6 GB, fastest 20B model).
Added Qwen 3.8 27B configuration for coding agents.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #clef
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I built an interactive stream overlay tool designed to run completely locally: AskAvatar 5.0.
Instead of static alerts, viewer tips & subs trigger reactive on-screen avatars with real-time AI voice replies.
Key focus was privacy and zero recurring costs: it runs directly on your machine via Ollama, so there are no cloud API fees or monthly subscriptions. Drops straight into OBS as a transparent window capture.
Check it out: https://AskAvatar.web.app
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I built an interactive stream overlay tool designed to run completely locally: AskAvatar 5.0.
Instead of static alerts, viewer tips & subs trigger reactive on-screen avatars with real-time AI voice replies.
Key focus was privacy and zero recurring costs: it runs directly on your machine via Ollama, so there are no cloud API fees or monthly subscriptions. Drops straight into OBS as a transparent window capture.
Check it out: https://AskAvatar.web.app
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I built an interactive stream overlay tool designed to run completely locally: AskAvatar 5.0.
Instead of static alerts, viewer tips & subs trigger reactive on-screen avatars with real-time AI voice replies.
Key focus was privacy and zero recurring costs: it runs directly on your machine via Ollama, so there are no cloud API fees or monthly subscriptions. Drops straight into OBS as a transparent window capture.
Check it out: https://AskAvatar.web.app
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I built an interactive stream overlay tool designed to run completely locally: AskAvatar 5.0.
Instead of static alerts, viewer tips & subs trigger reactive on-screen avatars with real-time AI voice replies.
Key focus was privacy and zero recurring costs: it runs directly on your machine via Ollama, so there are no cloud API fees or monthly subscriptions. Drops straight into OBS as a transparent window capture.
Check it out: https://AskAvatar.web.app
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The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
Small update: Run LLMs Locally
Added decision model Clef-Flash using llama.cpp.
https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
More about Clef from Cloudflare: https://blog.cloudflare.com/clef-decision-models/
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev #clef
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Small update: Run LLMs Locally
Added decision model Clef-Flash using llama.cpp.
https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
More about Clef from Cloudflare: https://blog.cloudflare.com/clef-decision-models/
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev #clef
-
Small update: Run LLMs Locally
Added decision model Clef-Flash using llama.cpp.
https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
More about Clef from Cloudflare: https://blog.cloudflare.com/clef-decision-models/
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev #clef
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Small update: Run LLMs Locally
Added decision model Clef-Flash using llama.cpp.
https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
More about Clef from Cloudflare: https://blog.cloudflare.com/clef-decision-models/
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev #clef
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New slides: Run LLMs Locally
Added decision models Laya, lev, Kev, OpenJev using llama.cpp.
Added testing with curl for TypeSafe System One API compatible endpoints.
Added text to speech with Qwen-3-TTS using llama.cpp.
Added Nemotron 3.5 Lightning.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev
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New slides: Run LLMs Locally
Added decision models Laya, lev, Kev, OpenJev using llama.cpp.
Added testing with curl for TypeSafe System One API compatible endpoints.
Added text to speech with Qwen-3-TTS using llama.cpp.
Added Nemotron 3.5 Lightning.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev
-
New slides: Run LLMs Locally
Added decision models Laya, lev, Kev, OpenJev using llama.cpp.
Added testing with curl for TypeSafe System One API compatible endpoints.
Added text to speech with Qwen-3-TTS using llama.cpp.
Added Nemotron 3.5 Lightning.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev
-
New slides: Run LLMs Locally
Added decision models Laya, lev, Kev, OpenJev using llama.cpp.
Added testing with curl for TypeSafe System One API compatible endpoints.
Added text to speech with Qwen-3-TTS using llama.cpp.
Added Nemotron 3.5 Lightning.https://github.com/thomas-0816/talks/blob/main/Run_LLMs_Locally_2026_ThomasBley.pdf
#ai #llm #llamacpp #stablediffusion #qwen3 #qwen3tts #nemotron #glm #localai #opencode #systemone #laya #lev #kev #openjev
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Longer-term take on Google’s Recorder app: a human in the loop remains needed
Over the last six months, one of the apps I’ve used most on my phone has been one of the few that I can only use on my phone–Google’s Pixel-only Recorder. The free voice-transcription app that I latched onto after ditching Evernote has become a regular part of my workflow, even as it’s made its limits more obvious to me.
Recorder’s core advantage remains the ability to work offline, which is good on both functional and privacy grounds. I’m stuck on horrible WiFi or in a cellular dead zone? No problem. A sensitive conversation with a source that I don’t want instantly uploaded to a cloud-based transcription service? Handled.
(That second scenario has yet to actually come up in my work; if, however, somebody summons me to a clandestine meeting in a garage in Rosslyn, it’s nice to know I’ll be ready.)
But maybe because this app has to rely on the processing power of the Pixel 9 Pro I bought nearly two years ago instead of borrowing any cloud smarts, it continues to make the same mistakes:
- Its speaker recognition is more of a cloud of probability; in one transcript of a four-person panel last week, the app IDed me as “Speaker 1,”Speaker 2,” and “Speaker 3” while labeling only one quote as coming from “Speaker 4.”
- It continue to capitalize random nouns–in particular, hitting its virtual Shift key for “Enterprise” when transcribing conversations about the computing needs of large organizations so often that I have to wonder if it was trained on Star Trek, German, or both.
- Sometimes it just skips a sentence or two or three. I don’t know how that’s possible, considering there’s no break in the recorded audio, unless maybe my phone’s processor got briefly swamped.
But even with these repeatable glitches, cleaning up a transcription rarely requires more than one playback while I have Recorder’s transcription open in an editing app–usually Google Docs, since that’s one of Recorder’s supported export options and since that Web app can itself work offline.
Google’s updates to the app haven’t yet made a meaningful difference in its transcription accuracy but have added the option to get an AI-generated summary of a transcript. That, too, works offline, and I should try that more often.
My overall take remains unchanged from what I wrote here in March: While this app could work better, I’d still want to/have to check any AI transcription against the original recording, so this one isn’t adding that much work. And it retains its non-trivial edge of costing nothing when I already pay enough for cloud services and can only expect those expenses to keep ratcheting up.
#AI #AITranscription #GoogleRecorder #localAI #onDeviceAI #Pixel #Pixel9Pro #Recorder #workOffline -
Longer-term take on Google’s Recorder app: a human in the loop remains needed
Over the last six months, one of the apps I’ve used most on my phone has been one of the few that I can only use on my phone–Google’s Pixel-only Recorder. The free voice-transcription app that I latched onto after ditching Evernote has become a regular part of my workflow, even as it’s made its limits more obvious to me.
Recorder’s core advantage remains the ability to work offline, which is good on both functional and privacy grounds. I’m stuck on horrible WiFi or in a cellular dead zone? No problem. A sensitive conversation with a source that I don’t want instantly uploaded to a cloud-based transcription service? Handled.
(That second scenario has yet to actually come up in my work; if, however, somebody summons me to a clandestine meeting in a garage in Rosslyn, it’s nice to know I’ll be ready.)
But maybe because this app has to rely on the processing power of the Pixel 9 Pro I bought nearly two years ago instead of borrowing any cloud smarts, it continues to make the same mistakes:
- Its speaker recognition is more of a cloud of probability; in one transcript of a four-person panel last week, the app IDed me as “Speaker 1,”Speaker 2,” and “Speaker 3” while labeling only one quote as coming from “Speaker 4.”
- It continue to capitalize random nouns–in particular, hitting its virtual Shift key for “Enterprise” when transcribing conversations about the computing needs of large organizations so often that I have to wonder if it was trained on Star Trek, German, or both.
- Sometimes it just skips a sentence or two or three. I don’t know how that’s possible, considering there’s no break in the recorded audio, unless maybe my phone’s processor got briefly swamped.
But even with these repeatable glitches, cleaning up a transcription rarely requires more than one playback while I have Recorder’s transcription open in an editing app–usually Google Docs, since that’s one of Recorder’s supported export options and since that Web app can itself work offline.
Google’s updates to the app haven’t yet made a meaningful difference in its transcription accuracy but have added the option to get an AI-generated summary of a transcript. That, too, works offline, and I should try that more often.
My overall take remains unchanged from what I wrote here in March: While this app could work better, I’d still want to/have to check any AI transcription against the original recording, so this one isn’t adding that much work. And it retains its non-trivial edge of costing nothing when I already pay enough for cloud services and can only expect those expenses to keep ratcheting up.
#AI #AITranscription #GoogleRecorder #localAI #onDeviceAI #Pixel #Pixel9Pro #Recorder #workOffline -
Longer-term take on Google’s Recorder app: a human in the loop remains needed
Over the last six months, one of the apps I’ve used most on my phone has been one of the few that I can only use on my phone–Google’s Pixel-only Recorder. The free voice-transcription app that I latched onto after ditching Evernote has become a regular part of my workflow, even as it’s made its limits more obvious to me.
Recorder’s core advantage remains the ability to work offline, which is good on both functional and privacy grounds. I’m stuck on horrible WiFi or in a cellular dead zone? No problem. A sensitive conversation with a source that I don’t want instantly uploaded to a cloud-based transcription service? Handled.
(That second scenario has yet to actually come up in my work; if, however, somebody summons me to a clandestine meeting in a garage in Rosslyn, it’s nice to know I’ll be ready.)
But maybe because this app has to rely on the processing power of the Pixel 9 Pro I bought nearly two years ago instead of borrowing any cloud smarts, it continues to make the same mistakes:
- Its speaker recognition is more of a cloud of probability; in one transcript of a four-person panel last week, the app IDed me as “Speaker 1,”Speaker 2,” and “Speaker 3” while labeling only one quote as coming from “Speaker 4.”
- It continue to capitalize random nouns–in particular, hitting its virtual Shift key for “Enterprise” when transcribing conversations about the computing needs of large organizations so often that I have to wonder if it was trained on Star Trek, German, or both.
- Sometimes it just skips a sentence or two or three. I don’t know how that’s possible, considering there’s no break in the recorded audio, unless maybe my phone’s processor got briefly swamped.
But even with these repeatable glitches, cleaning up a transcription rarely requires more than one playback while I have Recorder’s transcription open in an editing app–usually Google Docs, since that’s one of Recorder’s supported export options and since that Web app can itself work offline.
Google’s updates to the app haven’t yet made a meaningful difference in its transcription accuracy but have added the option to get an AI-generated summary of a transcript. That, too, works offline, and I should try that more often.
My overall take remains unchanged from what I wrote here in March: While this app could work better, I’d still want to/have to check any AI transcription against the original recording, so this one isn’t adding that much work. And it retains its non-trivial edge of costing nothing when I already pay enough for cloud services and can only expect those expenses to keep ratcheting up.
#AI #AITranscription #GoogleRecorder #localAI #onDeviceAI #Pixel #Pixel9Pro #Recorder #workOffline -
Longer-term take on Google’s Recorder app: a human in the loop remains needed
Over the last six months, one of the apps I’ve used most on my phone has been one of the few that I can only use on my phone–Google’s Pixel-only Recorder. The free voice-transcription app that I latched onto after ditching Evernote has become a regular part of my workflow, even as it’s made its limits more obvious to me.
Recorder’s core advantage remains the ability to work offline, which is good on both functional and privacy grounds. I’m stuck on horrible WiFi or in a cellular dead zone? No problem. A sensitive conversation with a source that I don’t want instantly uploaded to a cloud-based transcription service? Handled.
(That second scenario has yet to actually come up in my work; if, however, somebody summons me to a clandestine meeting in a garage in Rosslyn, it’s nice to know I’ll be ready.)
But maybe because this app has to rely on the processing power of the Pixel 9 Pro I bought nearly two years ago instead of borrowing any cloud smarts, it continues to make the same mistakes:
- Its speaker recognition is more of a cloud of probability; in one transcript of a four-person panel last week, the app IDed me as “Speaker 1,”Speaker 2,” and “Speaker 3” while labeling only one quote as coming from “Speaker 4.”
- It continue to capitalize random nouns–in particular, hitting its virtual Shift key for “Enterprise” when transcribing conversations about the computing needs of large organizations so often that I have to wonder if it was trained on Star Trek, German, or both.
- Sometimes it just skips a sentence or two or three. I don’t know how that’s possible, considering there’s no break in the recorded audio, unless maybe my phone’s processor got briefly swamped.
But even with these repeatable glitches, cleaning up a transcription rarely requires more than one playback while I have Recorder’s transcription open in an editing app–usually Google Docs, since that’s one of Recorder’s supported export options and since that Web app can itself work offline.
Google’s updates to the app haven’t yet made a meaningful difference in its transcription accuracy but have added the option to get an AI-generated summary of a transcript. That, too, works offline, and I should try that more often.
My overall take remains unchanged from what I wrote here in March: While this app could work better, I’d still want to/have to check any AI transcription against the original recording, so this one isn’t adding that much work. And it retains its non-trivial edge of costing nothing when I already pay enough for cloud services and can only expect those expenses to keep ratcheting up.
#AI #AITranscription #GoogleRecorder #localAI #onDeviceAI #Pixel #Pixel9Pro #Recorder #workOffline -
I’ve been experimenting with Bespoke Nimble, a local decision model running through Ollama.
It takes evidence, a question, and allowed answers at request time, so I can reuse it across classification tasks without training a model for each one.
I wrote about Nimble vs. Jev and traditional classifiers, plus how I’m using it for agent context pruning and game-economy balancing.
https://buthonestly.io/nimble-free-local-alternative-to-jev/
-
I’ve been experimenting with Bespoke Nimble, a local decision model running through Ollama.
It takes evidence, a question, and allowed answers at request time, so I can reuse it across classification tasks without training a model for each one.
I wrote about Nimble vs. Jev and traditional classifiers, plus how I’m using it for agent context pruning and game-economy balancing.
https://buthonestly.io/nimble-free-local-alternative-to-jev/
-
I’ve been experimenting with Bespoke Nimble, a local decision model running through Ollama.
It takes evidence, a question, and allowed answers at request time, so I can reuse it across classification tasks without training a model for each one.
I wrote about Nimble vs. Jev and traditional classifiers, plus how I’m using it for agent context pruning and game-economy balancing.
https://buthonestly.io/nimble-free-local-alternative-to-jev/
-
I’ve been experimenting with Bespoke Nimble, a local decision model running through Ollama.
It takes evidence, a question, and allowed answers at request time, so I can reuse it across classification tasks without training a model for each one.
I wrote about Nimble vs. Jev and traditional classifiers, plus how I’m using it for agent context pruning and game-economy balancing.
https://buthonestly.io/nimble-free-local-alternative-to-jev/
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The greatest shift in production AI isn't prompt engineering—it's Unix philosophy.
Instead of forcing a single monolithic LLM to act as philosopher, coder, and accountant, resilient architectures decompose tasks into lightweight local agent pipelines. One handles structured ingestion, one validates schemas, one executes within a deterministic Python sandbox.
Small tools connected by clean interfaces always win.
-
The question isn’t only what AI can do.
It’s who gets to decide when you can use it.
Access is not ownership. Using intelligence is not the same as controlling it.
Local models offer something more powerful:
AI as a capability you possess.
-
The question isn’t only what AI can do.
It’s who gets to decide when you can use it.
Access is not ownership. Using intelligence is not the same as controlling it.
Local models offer something more powerful:
AI as a capability you possess.
-
The question isn’t only what AI can do.
It’s who gets to decide when you can use it.
Access is not ownership. Using intelligence is not the same as controlling it.
Local models offer something more powerful:
AI as a capability you possess.
-
The question isn’t only what AI can do.
It’s who gets to decide when you can use it.
Access is not ownership. Using intelligence is not the same as controlling it.
Local models offer something more powerful:
AI as a capability you possess.
-
Running local AI models made one member's laptop run hot and slow. His solution: a custom fan-based cooling stand, now on version 2. The project also gave him plenty of practice with CAD modelling and splitting a large design for a smaller 3D printer.
#localai #3dprinting #cad #hackerspace #makerspace #openworkshop #MunichMakerLab #MuMaLab #Kreativquartier #Kreativlabor #DIYMunich
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Running local AI models made one member's laptop run hot and slow. His solution: a custom fan-based cooling stand, now on version 2. The project also gave him plenty of practice with CAD modelling and splitting a large design for a smaller 3D printer.
#localai #3dprinting #cad #hackerspace #makerspace #openworkshop #MunichMakerLab #MuMaLab #Kreativquartier #Kreativlabor #DIYMunich
-
Running local AI models made one member's laptop run hot and slow. His solution: a custom fan-based cooling stand, now on version 2. The project also gave him plenty of practice with CAD modelling and splitting a large design for a smaller 3D printer.
#localai #3dprinting #cad #hackerspace #makerspace #openworkshop #MunichMakerLab #MuMaLab #Kreativquartier #Kreativlabor #DIYMunich
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Running local AI models made one member's laptop run hot and slow. His solution: a custom fan-based cooling stand, now on version 2. The project also gave him plenty of practice with CAD modelling and splitting a large design for a smaller 3D printer.
#localai #3dprinting #cad #hackerspace #makerspace #openworkshop #MunichMakerLab #MuMaLab #Kreativquartier #Kreativlabor #DIYMunich
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KI lokal betreiben? Ich habe in den letzten Wochen an einem passenden Setup herumgetüftelt und bin mittlerweile sehr zufrieden. Mit dem aktuellen qwen3.8-27B Modell auf einem PC mit einer besseren Grafikkarte kann ich nun fast alles machen wofür ich früher #Claude Sonnet oder Opus gebraucht habe: Code Review, Fehler suchen und verbessern, übersetzen, READMEs schreiben oder etwas recherchieren. Hier in meinem Blogbeitrag gibt es die ganze Story: https://roland.alton.at/roland/rolog/ki-selbst-betreiben
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KI lokal betreiben? Ich habe in den letzten Wochen an einem passenden Setup herumgetüftelt und bin mittlerweile sehr zufrieden. Mit dem aktuellen qwen3.8-27B Modell auf einem PC mit einer besseren Grafikkarte kann ich nun fast alles machen wofür ich früher #Claude Sonnet oder Opus gebraucht habe: Code Review, Fehler suchen und verbessern, übersetzen, READMEs schreiben oder etwas recherchieren. Hier in meinem Blogbeitrag gibt es die ganze Story: https://roland.alton.at/roland/rolog/ki-selbst-betreiben
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KI lokal betreiben? Ich habe in den letzten Wochen an einem passenden Setup herumgetüftelt und bin mittlerweile sehr zufrieden. Mit dem aktuellen qwen3.8-27B Modell auf einem PC mit einer besseren Grafikkarte kann ich nun fast alles machen wofür ich früher #Claude Sonnet oder Opus gebraucht habe: Code Review, Fehler suchen und verbessern, übersetzen, READMEs schreiben oder etwas recherchieren. Hier in meinem Blogbeitrag gibt es die ganze Story: https://roland.alton.at/roland/rolog/ki-selbst-betreiben
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KI lokal betreiben? Ich habe in den letzten Wochen an einem passenden Setup herumgetüftelt und bin mittlerweile sehr zufrieden. Mit dem aktuellen qwen3.8-27B Modell auf einem PC mit einer besseren Grafikkarte kann ich nun fast alles machen wofür ich früher #Claude Sonnet oder Opus gebraucht habe: Code Review, Fehler suchen und verbessern, übersetzen, READMEs schreiben oder etwas recherchieren. Hier in meinem Blogbeitrag gibt es die ganze Story: https://roland.alton.at/roland/rolog/ki-selbst-betreiben
