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1000 results for “quantixed”
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Massive black holes drag and warp the spacetime around them in extreme ways. Observing these effects firsthand is practically impossible, so physicists look for laboratory-sized analogs that behave similarly. Fluids offer one such avenue, since fluid dynamics mimics gravity if the fluid viscosity is low enough. To chase that near-zero viscosity, experimentalists turned to superfluid helium, a version of liquid helium near absolute zero that flows with virtually no viscosity. At these temperatures, vorticity in the helium shows up as quantized vortices. Normally, these tiny individual vortices repel one another, but a spinning propeller — much like the blades of a blender — draws tens of thousands of these vortices together into a giant quantum vortex.
Here superfluid helium whirls in a quantum vortex.With that much concentrated vorticity, the team saw interactions between waves and the vortex surface that directly mirrored those seen in black holes. In particular, they detail bound states and black-hole-like ringdown phenomena. Now that the apparatus is up and running, they hope to delve deeper into the mechanics of their faux-black holes. (Image credit: L. Solidoro; research credit: P. Švančara et al.; via Physics World)
https://fyfluiddynamics.com/2024/05/black-holes-in-a-blender/
#astrophysics #blackHole #fluidDynamics #physics #quantumVortex #science #superfluid #superfluidHelium #vortices #vorticity
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Massive black holes drag and warp the spacetime around them in extreme ways. Observing these effects firsthand is practically impossible, so physicists look for laboratory-sized analogs that behave similarly. Fluids offer one such avenue, since fluid dynamics mimics gravity if the fluid viscosity is low enough. To chase that near-zero viscosity, experimentalists turned to superfluid helium, a version of liquid helium near absolute zero that flows with virtually no viscosity. At these temperatures, vorticity in the helium shows up as quantized vortices. Normally, these tiny individual vortices repel one another, but a spinning propeller — much like the blades of a blender — draws tens of thousands of these vortices together into a giant quantum vortex.
Here superfluid helium whirls in a quantum vortex.With that much concentrated vorticity, the team saw interactions between waves and the vortex surface that directly mirrored those seen in black holes. In particular, they detail bound states and black-hole-like ringdown phenomena. Now that the apparatus is up and running, they hope to delve deeper into the mechanics of their faux-black holes. (Image credit: L. Solidoro; research credit: P. Švančara et al.; via Physics World)
https://fyfluiddynamics.com/2024/05/black-holes-in-a-blender/
#astrophysics #blackHole #fluidDynamics #physics #quantumVortex #science #superfluid #superfluidHelium #vortices #vorticity
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New #review today: "In my review of their earlier release, The Call of a Crumbling World, I mentioned that it would be the first of a series of #InclusionPrinciple recordings scheduled for release in 2025; now a few months later, Clarino Oscura and Quantised Entanglement are out, and they couldn’t be more different from one another." #ExposeOnline #ImprovisedMusic #AvantGarde http://expose.org/index.php/articles/display/inclusion-principle-clarino-oscura-3.html
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@mariapopova I believe you correctly surmise “how staggered (Emily Dickinson’s) pliant young mind must have been to learn that scientists had just proven the existence of atoms.”
But #physicists didn’t accept the #AtomicTheory until #AlbertEinstein demonstrated its verity through Brownian motion over a *century* after #JohnDalton proposed it.
Both ‘particle #chemistry ‘ and #QuantumChemistry predate their #physics counterparts by a century since Dalton quantized *mass*!
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@mariapopova I believe you correctly surmise “how staggered (Emily Dickinson’s) pliant young mind must have been to learn that scientists had just proven the existence of atoms.”
But #physicists didn’t accept the #AtomicTheory until #AlbertEinstein demonstrated its verity through Brownian motion over a *century* after #JohnDalton proposed it.
Both ‘particle #chemistry ‘ and #QuantumChemistry predate their #physics counterparts by a century since Dalton quantized *mass*!
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@mariapopova I believe you correctly surmise “how staggered (Emily Dickinson’s) pliant young mind must have been to learn that scientists had just proven the existence of atoms.”
But #physicists didn’t accept the #AtomicTheory until #AlbertEinstein demonstrated its verity through Brownian motion over a *century* after #JohnDalton proposed it.
Both ‘particle #chemistry ‘ and #QuantumChemistry predate their #physics counterparts by a century since Dalton quantized *mass*!
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@mariapopova I believe you correctly surmise “how staggered (Emily Dickinson’s) pliant young mind must have been to learn that scientists had just proven the existence of atoms.”
But #physicists didn’t accept the #AtomicTheory until #AlbertEinstein demonstrated its verity through Brownian motion over a *century* after #JohnDalton proposed it.
Both ‘particle #chemistry ‘ and #QuantumChemistry predate their #physics counterparts by a century since Dalton quantized *mass*!
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@halama_immuno Here are a couple examples of #QuantumPoetry ; “Chemical Eye 👁️ on Songs of Innocence and Experience” 👉 http://www.sitnews.us/MacDougall/042909_macdougall.html
Sorry, it’s #chemistry not #physics. But since #JohnDalton was the first to propose a validated #QuantumTheory (he quantized mass in 1803), this toot fits the hashtag 💯.
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@halama_immuno Here are a couple examples of #QuantumPoetry ; “Chemical Eye 👁️ on Songs of Innocence and Experience” 👉 http://www.sitnews.us/MacDougall/042909_macdougall.html
Sorry, it’s #chemistry not #physics. But since #JohnDalton was the first to propose a validated #QuantumTheory (he quantized mass in 1803), this toot fits the hashtag 💯.
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@halama_immuno Here are a couple examples of #QuantumPoetry ; “Chemical Eye 👁️ on Songs of Innocence and Experience” 👉 http://www.sitnews.us/MacDougall/042909_macdougall.html
Sorry, it’s #chemistry not #physics. But since #JohnDalton was the first to propose a validated #QuantumTheory (he quantized mass in 1803), this toot fits the hashtag 💯.
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@halama_immuno Here are a couple examples of #QuantumPoetry ; “Chemical Eye 👁️ on Songs of Innocence and Experience” 👉 http://www.sitnews.us/MacDougall/042909_macdougall.html
Sorry, it’s #chemistry not #physics. But since #JohnDalton was the first to propose a validated #QuantumTheory (he quantized mass in 1803), this toot fits the hashtag 💯.
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CW: LLM use for dev (pi.dev as a slop newbie, long)
So yesterday I have actually tried https://pi.dev/ at home. In an isolated proxmox VM with a tight firewall as if dealing with dangerous bacteria 😱
And it kind of is. You can just let it install the tools it needs on Debian and all. It can modify its own configuration and tools and even write plugins for itself.
Magic yet frightening but it’s an isolated VM with only test projects. I access pi via ssh and let it do whatever it needs on its home VM. So far my laptop’s locale hasn’t been changed to zh_CN or ru_RU.
The only thing this could access on the local network was the local MLX / Ollama servers and I still felt surprised when it knew how to download a different model on my other machines via Ollama API using curl.
At the same time it feels easy to maintain control with the few set of basic read/write/bash tools it comes with. All controlled from a simple shell.
Your sessions are saved as text files and traceable and there are no hidden instructions in the prompt sent. I understand people complain about that in Claude (which I never tried). One thing I liked was asking summaries of what the fuck I was working on based on the session files. I am like that . I easily forget what I was doing even when I write the code myself.
As pi.dev (+lazypi) comes with some tools the context at startup of a new project easily goes to 18k tokens on first prompt (info on pi itself, on the tools and additional packages each add some kilobytes).
Even on further prompts speed on my local #LLMs was painful. They were ok to ask for snippets and chat locally (via OpenWebUI on another local VM). but not for so called agentic shit and rapid iteration.
So there went away my dream of seriously using pi.dev with my only local LLMs (on M1 Mac and Nvidia 3060 on PCs).
The whole idea is to save time so waiting minutes on a modification isn’t worth it, especially when trying to learn a system I don’t know. In retrospect some tests I did on the local LLM and took a full 4 minutes produced stuff that I think I wouldn’t produce in 4 minutes. But when learning to use this I jsut don’t want to wait four minutes on each test. Maybe with very careful planning it could make sense to just wait for local LLMs. And Yes the full LLMs produce better results than the quantized local versions when it gets complex. No surprise.
So I went full slopper and got a DeepSeek V4 account. It’s apparently 6x cheaper than American counterparts and most of all I’d rather be on the less worse side in the grand scheme of history. Also I am sure it’ll be working in 2027 after the US bubble pops. Yes I am a tankie in addition to a slopper now :-/
To make testing simple I asked “DeepSeek V4 flash” to build PHP sites and to configure nginx and PHP-fpm in the VM to serve it locally. The shit is fast. Much faster than me. It’s easy to feel overwhelmed by the fast pace of iteration. Remember I had planned nothing. Just went live testing and changing directions.
Summing up I went on for several hours basically reproducing a human translation web service that I used for years and now closed (icanlocalize). Basically editing strings in a SQlite db (and calling an api to translate stuff automatic) . The result is just so much pleasant (not hard icanlocalize was notably slow and confusing).
It’s a small tool only I would use (and maybe a colleague). I wouldn’t release this to the public but I can seriously use it on a local network for my own needs now.
Can’t deny that I alone would never have created something so functionally detailed with several screens, Ajax edition and handling of lot of edge cases in the same time and so easily iterated.
The machine tried to do some overly complex stuff sometimes and I was glad I actually knew how to write code (I think) to reorientate it and restructure the DB to always target simplicity.
Overall I must say there’s no reason I wouldn’t use this in the future. For such use cases. I never used this on Xcode as I don’t want to mess up my existing projects. It kind of feels like subcontracting except you can more easily cancel /rewrite stuff and iterate without waiting one day.
I am just sad I can’t use this purely locally as there’s no way the hardware I have or could reasonably get would be so fast.
pi.dev is local but I still sent all my very confidential data about this test PHP project to the CCP 🇨🇳.
That and about one dollar in token costs. It would have been less if I had understood earlier the /compact option. -
Building a Local-First Multi-Agent Orchestration Platform
The Problem with Cloud-Centric AI vs Local-First AI Orchestration
The cloud has long been the default stage for artificial intelligence. Frameworks such as LangChain, AutoGen, and CrewAI make it possible to orchestrate local or hosted models. However, their design still leans toward API-based, cloud-first execution. That approach works for experimentation, yet it introduces a clear weakness: dependence.
This return to autonomy echoes the early days of personal computing explored in Riding the Waves: From Home Computers to AI Orchestration, where individual control shaped innovation before the cloud era began.
From cassette tapes and floppy disks to orchestrated AI systems, computing has evolved through every wave.Every remote call carries both cost and exposure. Sensitive data must leave the machine to be processed elsewhere. Token-based billing discourages iteration. Even when using secure endpoints, developers trade autonomy for convenience. As a result, innovation is often limited by infrastructure.
A local-first approach changes that balance. It focuses on privacy, predictability, and cost control by running agents directly on local hardware. The cloud remains useful for large or complex tasks, yet local processing gives developers freedom. It does not reject connectivity; instead, it restores choice.
That principle guided the creation of a production-grade orchestration platform of roughly 3,700 lines of Python. Through seven BDD development cycles and a 96.5 percent test pass rate, it proved that a reliable system can run with zero external dependencies. Using SQLite and JSONL metrics, the same codebase coordinates multiple AI agents securely, predictably, and locally across devices.
Three-Layer Architecture of a Local-First AI Orchestration Platform
The system follows three clear layers: CLI, Orchestrator, and Registry. Each layer handles a specific function in the orchestration lifecycle.
The CLI layer, built with Typer, serves as the command surface. It offers more than twenty commands and about six hundred lines of code. Developers can initialize environments, run agents, and invoke workflows. This layer is the human-facing edge of the platform.
The Orchestrator layer, written with FastAPI, acts as the control center. It manages scheduling, routing, and task lifecycles. Its asynchronous design lets small tasks run in parallel while heavy inference jobs are handled one at a time. The main application file stays compact and easy to read.
The Registry layer defines intelligence. Eleven expert agents are declared in Pydantic configurations that describe capabilities, dependencies, and budgets. New agents can be added or updated with simple configuration changes.
FastAPI was chosen for its async speed and automatic schema generation. SQLite replaced Redis to stay aligned with the local-first approach. JSONL metrics were selected for their simplicity and transparency. As a result, commands call APIs, APIs invoke agents, and agents return results through a steady feedback loop.
These principles align with the broader ethical and security implications discussed in AI Orchestration, Security, and the Future of Work, where resilience and accountability shape the next phase of automation.
Hardware-Aware Resource Scheduling in a Local-First AI Orchestration Platform
Local-first systems must respect hardware limits. Machines differ widely: some are laptops with integrated GPUs, while others are workstation-class servers with up to 128 GB of RAM and powerful GPUs. Consequently, the orchestrator adapts through hardware-aware scheduling.
Each environment selects one of three profiles: Laptop, Workstation; or Server, defined in a simple
resources.yamlfile:profile: workstation max_agent_runs: 4 gpu_memory_limit: 16000 cpu_cores: 8
During initialization, the active profile sets concurrency gates and resource budgets. Lightweight operations run together, while heavy tasks acquire locks before execution. A dual-lock system separates general resource tracking from expensive AI calls. This method maintains parallel work without conflict.
Scheduling moves through five stages: global concurrency check, CPU allocation, GPU budgeting, codex serialization, and cleanup. Each stage keeps the system predictable and stable. Cleanup routines always release resources, even after errors.
This approach brings precision and balance to orchestration rather than experimentation.
Despite these advantages, running a local-first AI orchestration platform introduces its own constraints. The system’s performance depends directly on available hardware, and smaller machines may need to rely on compact or quantized models such as Phi or Llama variants instead of large-scale cloud models. This balance between efficiency and accuracy requires careful model selection. In addition, while workstation-class setups with 128 GB of RAM can handle concurrent agents with ease, laptops or limited servers may experience slower inference or constrained multitasking. These realities remind developers that local-first design is not about matching the cloud’s abundance, but about achieving sustainable autonomy within real hardware boundaries.
Integrating the Model Context Protocol (MCP)
While a local platform values privacy, it still needs secure communication. The Model Context Protocol (MCP) provides structured interoperability for tools that observe or influence AI workflows.
The implementation, only 254 lines of code, supports two authentication modes: simple tokens for development and shared-secret tokens for production. It runs across HTTP, WebSocket, and TCP. As a result, the system remains flexible yet secure.
Through the MCP tool system, external services can register abilities such as
memory.readormemory.write. These allow dashboards, IDEs, or bots to stream workflow events in real time. For example, a Grafana panel can show resource usage, while an IDE plugin can display agent progress.In short, MCP turns a local orchestrator into a cooperative system—connected when needed, private by default.
For a deeper exploration of how MCP enables cross-agent collaboration, see Unlocking AI Collaboration with the Model Context Protocol.
A symbolic visual of the Model Context Protocol: where developer flow, memory, and modular context converge.
DAG-Based Workflow Execution
At its heart, orchestration is dependency management. The platform models workflows as directed acyclic graphs (DAGs), where each node represents a task and edges define dependencies.
A common configuration is:
plan → (backend, frontend) → (security, qa)
The product manager agent drafts a feature plan. Backend and frontend agents work in parallel. Security and QA agents then validate results. Prompts reuse earlier outputs through simple placeholders like
{backend.result}. The queue engine runs each step, stores results, and queues the next tasks until completion.This design preserves context, improves traceability, and supports recovery from partial failure. This emphasis on context-driven execution mirrors insights from AI Agents and Large Codebases: Why Context Beats Speed Every Time.
The Three-Tier Guardrail System
Stable orchestration requires discipline. Therefore, the platform applies a three-tier guardrail system.
- Input validation filters unsafe or malformed prompts.
- Runner control manages retries and captures runtime errors.
- Output checks reject empty or inconsistent responses.
All guardrail events are logged in
guardrail_metrics.jsonlwith categories such asguardrail_block,runner_error, andvalidator_block. Developers can view them directly:python -m agents.cli.main metrics guardrail --details 5
As a result, every failure becomes visible and fixable. Silent issues disappear.
The Eleven Expert Agents
Intelligence resides in the registry of eleven expert agents. They are grouped into development, security, and infrastructure domains.
- Development:
product_manager,bdd_backend,bdd_frontend,qa - Security:
security,validator,guardrail - Infrastructure:
database,networking,web3,encryption
Each agent includes a Pydantic schema defining its role and resource limits. During startup, these definitions convert to runtime specifications. This clear separation keeps the system flexible. Moreover, every action is logged, ensuring full transparency.
Built-In Web Dashboard
Transparency should not require the cloud. Instead, the platform provides a lightweight local web dashboard with seven views: system overview, workflows, guardrails, resources, agent timeline, MCP clients, and JSON API.
Each page loads in under 100 milliseconds and refreshes automatically. It remains responsive, simple, and always available—even offline.
Context Management and Memory
Persistent context keeps intelligence coherent. The SQLite-backed memory system uses two tables:
memoryfor key-value data andhistoryfor append-only logs.Agents use REST or MCP calls to read and write context. This lets long workflows maintain state between runs. As a result, agents can recall past outputs or user preferences without external storage.
Developer Experience and Automation
Starting up is simple:
python -m agents.cli.main init --profile laptop
This single command creates all configuration files, chooses a hardware profile, and prepares directories. The CLI also scaffolds projects in five languages: Python, Go, React, PHP, and Perl. Each uses templates with variable substitution for fast setup.
With more than twenty commands and six sub-apps, Typer provides clear and self-documented interfaces. Consequently, the CLI becomes both toolkit and guide.
A BDD-Driven Development Journey
Development followed seven BDD cycles, each improving a key feature:
- MCP authentication and security
- Zero-friction initialization
- API deduplication
- Resource scheduling
- Dashboard observability
- Advanced resource tracking
- Fail-fast initialization
Each cycle used RED-GREEN-REFACTOR testing and generated living Gherkin documentation. As a result, coverage now exceeds 85 percent, keeping behavior predictable while features evolve.
A visual metaphor of how structured thinking, like Gherkin and Behavior-Driven Development, helps AI systems connect human intent with machine execution.
The importance of clear behavioral documentation aligns closely with ideas from AI, Gherkin, and the Future of Software Development: Why Behavior-Driven Development Matters.Production Readiness and Lessons Learned
The final system demonstrates production-level quality. It includes thread-safe scheduling, clear error handling, and real-time monitoring. JSONL metrics make audits simple. Configuration is idempotent and safe to repeat.
Key technical innovations include:
- Fail-fast error handling with clear fixes
- Append-only metrics for transparency
- Dual-lock control for parallel work
- Hot-swappable agent settings
- Hardware-aware scaling across profiles
Building locally highlighted several truths. Simplicity brings reliability. In addition, insight into system behavior is essential. Developer experience shapes success as much as model accuracy. Above all, privacy and control can align with capability.
The platform now runs seamlessly across laptops, workstations, and servers. Each profile is tuned to its limits, and each agent knows its role.
The Future of Local-First AI Orchestration Platforms
The local-first AI orchestration platform proves that autonomy and performance can coexist. It respects hardware, protects data, and offers hybrid flexibility. In practice, it shows that orchestration can be as private as computation itself. This serves as a foundation for tools that return control to their builders.
Next comes refinement: wider support for edge devices, stronger context management, and closer integration with ecosystems such as Claude CLI and OpenAI APIs. Although the system is already production-grade, its deeper importance lies in the idea it represents: local-first intelligence as a craft, not a slogan.
The cloud will always have its place. However, it should never be the only place. Ultimately, true orchestration begins where control is personal.
The next frontier of AI engineering will not be written in the cloud alone. It will emerge from local workstations, developer labs, and edge devices where privacy and autonomy coexist. If this vision of local-first orchestration resonates with your work or research, share your thoughts, build upon the concept, or join the discussion on how to design systems that respect both hardware and humanity. Real progress begins when we question the defaults and start building differently.
What is a local-first AI orchestration platform?
A local-first AI orchestration platform manages multiple AI agents directly on local hardware instead of relying on cloud APIs. It improves privacy, reduces cost, and increases control over performance.
How does hardware-aware scheduling improve AI orchestration?
It adapts task execution to available resources such as CPU cores and GPU memory, ensuring stability on devices ranging from laptops to 128 GB workstations.
What role does the Model Context Protocol (MCP) play?
MCP enables secure communication between agents and external tools, allowing dashboards and IDEs to interact with workflows in real time while maintaining local control.
Can local-first systems replace cloud orchestration entirely?
Not completely. The cloud remains valuable for large-scale training and inference. Local-first orchestration complements it by offering autonomy, speed, and privacy for smaller or sensitive workflows.Key Takeaways
- A local-first AI orchestration platform enhances autonomy, privacy, and cost control by running AI agents directly on local hardware.
- It features a three-layer architecture: CLI for commands, Orchestrator for task management, and Registry for defining agent intelligence.
- The platform employs hardware-aware scheduling to optimize performance based on device capabilities, such as laptops or servers.
- The Model Context Protocol (MCP) facilitates secure communication between agents and external tools while maintaining local control.
- Its future includes support for edge devices and deeper integration with existing ecosystems, emphasizing personal control over AI workflows.
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“Scuse me while I kiss the sky”*…
In 1967, Jimi Hendrix’s manager, Chas Chandler arranged for Jimi to meet Cream…
There was a particular night when Cream allowed Jimi to join them for a jam at the Regent Street Polytechnic in central London. Meeting Clapton had been among the enticements Chandler had used to lure Hendrix to Britain: “Hendrix blew into a version of [Howlin’ Wolf’s] ‘Killing Floor’,” recalls [Tony] Garland, “and plays it at breakneck tempo, just like that – it stopped you in your tracks.” [Keith] Altham recalls Chandler going backstage after Clapton left in the middle of the song “which he had yet to master himself”; Clapton was furiously puffing on a cigarette and telling Chas: “You never told me he was that fucking good.” – source
Hendrix’s extraodinary virtuosity has, altogether justly, gotten a great deal of attention; less well noted, his incredible mastery of the technology of music making, recording, and performance. Rohan Puranik explains…
3 February 1967 is a day that belongs in the annals of music history. It’s the day that Jimi Hendrix entered London’s Olympic Studios to record a song using a new component. The song was “Purple Haze,” and the component was the Octavia guitar pedal, created for Hendrix by sound engineer Roger Mayer. The pedal was a key element of a complex chain of analog elements responsible for the final sound, including the acoustics of the studio room itself. When they sent the tapes for remastering in the United States, the sounds on it were so novel that they included an accompanying note explaining that the distortion at the end was not malfunction but intention. A few months later, Hendrix would deliver his legendary electric guitar performance at the Monterey International Pop Festival.
“Purple Haze” firmly established that an electric guitar can be used not just as a stringed instrument with built-in pickups for convenient sound amplification, but also as a full-blown wave synthesizer whose output can be manipulated at will. Modern guitarists can reproduce Hendrix’s chain using separate plug-ins in digital audio workstation software, but the magic often disappears when everything is buffered and quantized. I wanted to find out if a more systematic approach could do a better job and provide insights into how Hendrix created his groundbreaking sound.
My fascination with Hendrix’s Olympic Studios’ performance arose because there is a “Hendrix was an alien” narrative surrounding his musical innovation—that his music appeared more or less out of nowhere. I wanted to replace that narrative with an engineering-driven account that’s inspectable and reproducible—plots, models, and a signal chain from the guitar through the pedals that you can probe stage by stage…
[And probe it Puranik does– fascinatingly, stage by stage…]
… Hendrix didn’t speak in decibels and ohm values, but he collaborated with engineers who did—Mayer and Kramer—and iterated fast as a systems engineer. Reframing Hendrix as an engineer doesn’t diminish the art. It explains how one person, in under four years as a bandleader, could pull the electric guitar toward its full potential by systematically augmenting the instrument’s shortcomings for maximum expression.
“Jimi Hendrix Was a Systems Engineer,” from @spectrum.ieee.org.
See also: “The Technology of Jimi Hendrix.”
* Jimi Hendrix, “Purple Haze”
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As we plug in, we might send well-connected birthday greetings to another wizard with wires, Geoff Tootill; he was born on this date in 1922. An electronic engineer and computer scientist, he worked (with Freddie Williams and Tom Kilburn) to design a computer memory. To that end they built the first electronic stored-program computer— the Manchester Baby— at the University of Manchester in 1948.
The Baby was not intended to be a practical computing engine, but was instead designed as a testbed for the Williams tube, the first truly random-access memory. Nonethless, Baby worked: Alan Turing moved to Manchester to use it, and the following year, it inspired the Ferranti Mark 1, the world’s first commercially available electronic general-purpose stored-program digital computer.
#computerMemory #computing #culture #electronics #engineering #GeoffTootill #history #JimHendrix #ManchesterBaby #music #Technology -
I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows: 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appears set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
Another downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by spending a few hundred bucks on a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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@nickbearded i like looking at the llm leaderboard but lately ai wise i have done this - got exo/exolabs clustering app going, tried a model with llamafile and am investigating localai - they have p2p, federation so groups of people can focus interence and training on that sectors siloed and specialized data...
localai has had p2p ai for like 10 mos - being able to run it on a couple boxes cpu only and offload embeddings securely would be nice
you probably should run a gpu or two or three in your cluster but it is not totally necessary - you can process embeddings/tokens for local data inclusion into vector db and do like 1tb in 6 days vs the job taking over a month cpu only
I like the idea of setting up federated group to match up with portals for anything but for specific biz sectors and verticals could be v helpful cause then clients could have p2p ai data lake with relevant topical biz data - it is basically what i alluded to in 90 pt plan but now much more concrete
How Much Can You Get Done in a Few Days?
In a weekend or 3–4 focused days, with a few machines you can:
✅ Spin up LocalAI on 2–4 nodes
✅ Join or form a federated network
✅ Deploy several quantized LLMs, image generators, or audio models
✅ Run small-batch inference jobs across the network
✅ Offload some heavy jobs (like summarization, embeddings) to swarm partners
✅ Start offering services like local search, chatbot assistants, or automated data pipelines for your bizWith good orchestration, you can match or exceed what a $500–1,000/month cloud bill would buy. >>>this is doubtful initially but could be a productivity boost and help sales when people see there is a lot of industry specific info
#unused cycles
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Can we talk about the future that oligarchs actually want, vs the one they tell you they want?
And the mechanical elephant in the room: The #automationparadox.
The automation paradox goes like this:
"We are going to automate jobs with robotics and #AI. You won't need to work. We'll do something like Universal Basic Income (UBI), I guess. Or whatever."That's not what the oligarchs actually 'want', it's the outcome of their actual want: To eliminate the cost of human labor and increase profit. But that ends in a paradox.
Who buys the goods and services if the fruits of human labor do not result in income?
My thesis is this: "You still do."
That works not by removing income completely, but by quantizing ALL income (except their own) so that we, the masses, are ALL equally poor. Not so poor that we cannot still buy a new Smart TV, a new smartphone, pay for media subscriptions for that new Smart TV. Captive and controlled consumers. Money, wealth, becomes less about the amount of currency* than about the level of control, power, and privilege. Control of access to energy is related topic.
Quantizing income takes the excess that people who are doing better than average, and people who are considered 'rich' but not yet powerful, etc. - taking that and leveling that playing field down to a mean. That above-the-line public wealth then goes to the oligarchs. This 'wealth' actually equates more to power than money, but that's a whole 'nother post.
The #oligarchy who poo-poos socialism wants to institute what I'm calling Capitalistic Communism; replacing the state with the Oligarchy. The people are, as in Soviet-style communism, equally depressed financially. No one, except the pets of the state (Oligarchs) is more affluent than anyone else. And most will still need to work in some form or another. But never with any ability to rise above the quantized floor of 'allowed income'. Or the ability to own _anything_.
The oligarchy will extract the remaining wealth from the middle, upper-middle, and upper class, leaving only The Masses on one end and The Oligarchs on the other.
And then we will well and truly be the consuming slaves they 'want'.
* It already is, Elon Musk doesn't 'make money', he accumulates financial control.
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows: 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appears set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
Another downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by spending a few hundred bucks on a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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The body transforms into a series of data points, its essence quantified within the digital marketplace.
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https://bitenote.eu — my calorie tracker inside Telegram — got better:
→ public API
→ Withings integration
→ -20% on Premium with code TAROT (don't ask)Fediverse-only bonus: code FEDIRULES = +2 weeks PRO on top.
#buildinpublic #indiehackers #healthtech #telegram #quantifiedself #fediverse
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On a testé l'Hôtel du Lion Rouge ce soir ! 🦁
Petit jeu de rôle sympa à narration partagée, c'est plus varié qu'un For the Queen, et certaines cartes sont très fun à jouer.
Par contre à 6, il faut vraiment se limiter à un tour de table par carte, sinon la quantité d'éléments posés explose 😅.
@comemartin : on a fait à notre sauce pour l'épilogue parce qu'on a pas compris la règle. Comment se fait le décompte du nombre de mentions par fil rouge ?
Merci pour le jeu !
#JDR #GMless -
New header picture. Loss and Damage Finance Now!
Climate activists call for rich polluting countries and corporations to pay up for #LossAndDamage.
👀 UNFCCC June Climate Meetings (SB 62)
16 Jun - 26 Jun 2025New Collective Quantified Goal #NCQG
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Connaissez-vous des magazines en anglais pour les tout-petits (type Picoti des éditions Milan) qui soient recevables en France ?
De préférence avec des personnages féminins en quantité égales aux personnages masculins et non stéréotypés et même chose pour les personnages non-blancs.
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D'après Hadeel, l'hôpital de #MSF le plus proche serait à au moins trois quart d'heure en voiture. Faute de moyen de déplacement et de pouvoir les faire venir, et dans l'attente de trouver une solution, elle a besoin de se fournir en médicaments capables de traiter la diarrhée et faire face aux douleurs. Ainsi qu'en eau potable en quantité suffisante pour faire face à la déshydratation.
Pouvez-vous svp partager ce message voire faire un don svp ? :boost_request: 🇵🇸🕊️🙏
Voici le lien de sa cagnotte, tout montant accepté
https://chuffed.org/project/177026-help-hadeels-family-survive-in-gaza2/2
#MutualAidRequest #EmergencyMed #Gaza #HelpGaza #SaveAChild
@aral @gazaverified @simon_brooke -
Gaza : MSF dénonce la « privation délibérée d’eau infligée aux Palestiniens » par Israël
La pénurie « est telle qu’il est tout simplement impossible de fournir des quantités suffisantes à la population », soutient MSF.
En mars 2026, MSF fournissait plus de 5,3 millions de litres d’eau par jour, l’équivalent des besoins minimaux de plus de 407 000 personnes, soit environ un habitant sur cinq.
« Les ordres de déplacement imposés par l’armée israélienne ont empêché les équipes de MSF d’accéder à des zones où elles fournissaient de l’eau à des centaines de milliers de personnes »
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WoW ! De la magie blanche dans les ténèbres matinales 🤩
Une petite bordée d'une dizaine de cm au Centre-de-la-Mauricie.
«Si vous choisissez de ne pas avoir de la joie dans la neige, vous aurez moins de joie dans votre vie mais avec la même quantité de neige.» (Nathalie Bisson @madamepacedubonheur “Le Pace du Bonheur - Courir et Vivre pour Soi”, p. 165)
Bon lundi 🙂 On lâche pas 👍
Temp. 1°C Vent: NE km/h
#running #Jogging #neige #snow #automne #Shawinigan #Mauricie #Quebec
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Nvidia N1 exposto: Fuga de imagens revela a nova motherboard com uma quantidade absurda de memória
🔗 https://tugatech.com.pt/t81590-nvidia-n1-exposto-fuga-de-imagens-revela-a-nova-motherboard-com-uma-quantidade-absurda-de-memoria