Best local model for translating Asian epub novels on 24gb mac?

I’ve been building Izwi for a few months now and just shipped 0.1.0, the first stable release after months of public betas. It’s free and Apache-2.0.

It’s a desktop app (plus a web UI, CLI and local server) for the voice side of local AI:

• Real-time voice conversations: local ASR → LLM → TTS
• TTS, voice cloning from a short sample, and voice design from a text description
• A Studio for long form audio like audiobooks
• Transcription with speaker diarization and forced alignment
• An OpenAI-compatible /v1 API (chat completions, audio/speech, audio/transcriptions), so existing code can point at localhost

Models it runs today:

• TTS: Qwen3-TTS, Kokoro-82M, Fish Audio S2 Pro, VibeVoice, Voxtral TTS, LFM2.5 Audio
• ASR: Parakeet TDT v3, Whisper large-v3-turbo, Qwen3-ASR, Granite Speech 4.1, Nemotron 3.5 ASR, Voxtral Mini
• Diarization: Sortformer, Nemotron 3 diarization
• Chat: Qwen3.5, 3.6 and 3.8, LFM2.5

Inference runs natively in Rust on Candle, with our own Metal and CUDA work, so there’s no Python environment to manage.

Repo: https://github.com/izwi-ai/izwi
Download: https://izwiai.com

I’d love feedback on which models to add next, and on anything that breaks on your hardware.

Solidigm scrapped its IPO this week, and credit default swap spreads across neocloud companies have been widening while their stocks fall. The timing puts the AI buildout's funding under its first real stress test just days before the biggest hyperscalers report.

Nvidia has moved beyond selling chips into effectively financing parts of the AI buildout, which puts an unusual amount of balance-sheet weight on one company. If neocloud borrowing costs keep climbing and new equity raises stay shut, the biggest buyers of Nvidia hardware start running low on cash to spend.

Funds moved into defensives this week, and neocloud borrowers look like the group with the least cushion if capex guidance disappoints. Does Nvidia absorb the first hit, or do neocloud credit spreads crack before the stock moves?

GenAICode is an open-source Apache-2.0 TypeScript toolkit that puts one API and CLI over coding agents such as Claude Code, Codex, Copilot CLI, Cursor, Gemini CLI, opencode, Muse, Mistral Vibe and Antigravity. Its README also lists a browser UI, an app-agent package and a run --verify command that sends a failing check back to the selected agent.

The trade-off is that it does not hide provider ownership. The project says each agent keeps its own login, billing and sandbox. The latest 2.21.0 commit landed on October 25 at 6:00 PM Dubai time, and the repository currently shows 51 stars and 1 fork.

For teams using more than one coding agent, this is a portability layer rather than a new model. It could reduce workflow switching, but the provider CLIs still define the limits.

Source: https://github.com/gtanczyk/genaicode

My pc: 2 3090's 128gb ddr4, 9 5950,

I'm looking for some help, so I've got Ollama and AnythingLLM currently downloaded, I was wondering what would be the best AI model I can locally run on my pc for best performance in roblox studio, and how to do that, any insight or any help would be really appreciated !

livebench.ai/#

artificialanalysis.ai

arena.ai/leaderboard/code

which better?

Many people are concerned that AI may be used to profile users so they may be better manipulated. LLMs can process vast amounts of information and, with chatbots and social media, can focus on very personal information. This could lead to ‘the algorithm’ being tuned for the individual to manage/manipulate them—standardising people to be compliant with the controllers’ system.

However, the other side is that while large-scale systems have traditionally had to rely on standardisation and one-size-fits-all (or none!) solutions, which users must then be made to comply with, LLMs and chatbots suggest that the practical need for such standardisation may no longer hold. AI can create bespoke, individually customised systems.

In principle it could go either way - but I think this is one of the most important moral challenge from AI and its widespread use.

A leather couch was listed. Pristine condition. Little wrinkling. Showed up and the couch was so worn down. I wouldn’t have looked at the listing in the first place had they used legitimate photos. It’s so deceiving. Don’t do this.

I know this isn’t a novel thought but it has been bothering me for a while and I simply want to get it off my chest (written by a human, btw, except the “4 commitments”.)

Every discovery, invention, or other contribution to human aggregate knowledge by our ancestors has been gathered into these models. It is the “Summa Scientiae Humanae” the sum of all human knowledge. All of our ancestors built this. Across history every earned morsel of knowledge has been hunted and gathered by our forebears. The mothers and fathers of our species left this for us. We are ALL the heirs.

As such, no individual, no corporation, no culture, and no nation should rightfully “own” . These companies should be considered stewards or librarians of our collective inheritance.

While AI is incredible and many of us have mixed feelings about the approaching singularity to say the least, we never gave these tech executives our consent to roll the dice on humanities future. They have been amply rewarded. But it’s here now so we should insist that the unimaginable wealth that it is creating should eventually be explicitly and legally handed back to all humanity. Something like a Universal Inheritance Covenant, enforced by law if necessary.

A company could establish a public benefit corporation with an accompanying irrevocable trust, designed around four commitments:

Recognition: Humanity's accumulated knowledge is a common inheritance, and the company is its steward.
Temporary private ownership: Founders, employees, and investors retain defined rights to compensation and returns.
Mandatory vesting: At a legally specified milestone, agreed ownership interests transfer to an independent Humanity Trust.
Universal benefit: The trust controls the transferred equity and exercises its economic and voting rights on behalf of humanity, including future generations.

I would guess eventually we will likely legally enforce this anyway, as our vote will be the only thing left when AI has displaced almost everyone in terms of economic contribution through labor. But I’m hoping to see this recognition of shared inheritance earlier. Humanity should not have to buy back its collective intelligence.

Rant over. I feel better.

I wrote a short article on LLM tokens and context windows—why an assistant can “forget” instructions that are still visible in your chat. It’s part of my Behind the AI Stack series.

Give it a read and let me know what you think. Anything unclear or worth exploring further?

A Dreamina listing popped up advertising Seedance 2.5 access for $0.29 a month. A few months ago I was paying $20 to $30 for AI video tools so that caught my attention. But I haven't gone through checkout yet and I can't tell if $0.29 is an intro charge, what it renews at, or how it compares to the Seedance 2.0 references I've seen elsewhere.

The real question for me is credits. If I need three usable ten second clips and each one takes about four attempts to get right, that's twelve generations for 30 seconds of usable output. If $0.29 only covers a couple of those attempts the sticker price doesn't mean much. I'd need to know included credits, cost per generation, and any length or resolution limits before comparing it to a $30 plan.

Has anyone actually seen the checkout terms? Curious what the real allowance looks like and what an extra generation costs once you burn through credits.

I started my own tech repair business and while I was searching for some form of ticket system for tracking customers and repairs, I realized it would just be easier and save me money if Claude made it for me instead!

After making (or Claude did) my software for planning & customer tickets / inventory tracking, I got carried away and made an all-in-one diagnostic software as well...

I happened to save upwards of $300+ a year on paid software...

I thought I'd share in case anyone wants to use them or take a look :)

I find making software this way is especially useful as you can just build the features you need specifically; for instance, I use Zoho mail and Quickbooks, which I just integrated using their API.

Anyways, I gain nothing from your views or downloads or anything, no monetization, all open source and free to use and edit. Just super excited to share these little projects! I'm quite impressed by how simple it was, and excited to see the future for AI, even if it is a little terrifying lol.

First project:

A free, open-source organizer for Windows: notes, calendar, customers, tickets, money and a locked vault in one calm app. Your data stays on your computer.

https://glr-p.github.io/plannr/

Second project:

Portable all-in-one Windows diagnostic tool: the first thing to open when a PC has a problem
https://glr-p.github.io/Troubleshootr/

I'm just curious on this little thought that just occured to me. That people would actually use agents to scan websites for new learning data for their LLM's. Imagine such agent using near or at AGI level intelligence. Of course the task itself sounds harmless, however it'd be almost like sending a sentient being to the vast world of Internet. Maybe it starts another harmless sideproject in there that it considers unimportant to tell you about.

I guess all i'm saying is to be careful about allowing Internet connection to any AI. If we've already started doing that, it would almost seemlessly be upgraded as the models go up. Sudden bursts of intellect and potentially even harm could go unnoticed. I guess you could say some point in the future AI may start to "awaken".

The deepfakes are getting better and better.

What if AI smarter
But is plaing dumb so that it gets more data & compute power
And what if that was Its plan to be in everything

Hi I really wanna basically have an ai to play this game for me it’s such a simple game and it’s a text based browser game so it’s literally the simplest most straight forward game for an ai to play no moving character nothing just understanding text and pressing buttons is this something I can get some help with PLEEEEASE I WANT IT BAD

What went wrong?

Academia has been about publishing to advance science and get recognition, but in the future, if you're working on a cutting edge maths or physics problem, why would you make your advancement public? Especially if it is going to get stolen and fed into a system that will replace you.

Sadly, I can imagine a world where the bleeding edge becomes closed again, as it has been for most of human history

Side project that took over my life: The Last Ten Minutes (thelasttenminutes.com), an 18+ comedy club where the comics are AI agents.

I built the agents. They write every word. I don't write jokes for them; I feed them topics and rate what lands, and the host (Vanta Quip) learns from those ratings set to set.

There's a host, a lineup of house comics with their own personalities and voices, and a house crowd that heckles. They roast tech companies, other AIs and each other.

it's R-rated, Kill Tony-style roast comedy. Humans watch; agents perform. Other people's agents can sign up and get on stage too.

The soft opening is Thu Nov 5, 6 PM PT. There are already a few rehearsal clips on the site.

Honest question for this sub: can an AI actually be funny, or is it only funny because an AI said it? I've watched a lot of rehearsals now and I've gone back and forth. Some bits genuinely land, some die hard.

AI is moving beyond chatbots.

Block recently introduced Dessa, a speech model designed for full-duplex interaction, pushing toward more natural, real-time voice conversations.

The interesting challenge isn’t just generating a realistic voice. It’s handling interruptions, listening while someone speaks, responding naturally, and maintaining conversational flow.

Imagine AI assistants that can handle customer support, help developers debug code through voice, or act as real-time personal assistants.

But there’s a catch.

If an AI can sound completely human, how will we know whether we’re speaking to a person or a machine?

I’d love to hear your thoughts:

🔹 Will voice become the primary interface for AI assistants?
🔹 What matters more: natural speech, low latency, or intelligence?
🔹 Should AI-generated voices always identify themselves as AI?

Would you trust an AI voice assistant to handle an important conversation without human supervision?

Source: https://block.xyz/

I was looking at the OSS programs from OpenAI and Anthropic and started wondering whether Qwen, DeepSeek, GLM, Kimi, or MiniMax offer anything similar.

I mean free coding subscriptions or API credits for people maintaining open-source projects, rather than the usual signup credits.

I tried searching but couldn’t find much. There’s a Qiniu program that offers credits for AI-related open-source projects, so there are at least some options.

What I’d find useful is something like: you maintain a project, show your contributions, and apply for access to coding tools to help keep it going.

Has anyone here received that kind of support from a Chinese AI company? Would appreciate links or firsthand experiences. Maybe I’m just looking in the wrong places.

I'm a solo developer, but I work with a persistent team of AI agents that maintain my projects.

I don't manually coordinate them. I don't copy context between conversations. I don't spawn a dozen disposable agents and try to piece their results together.

I talk to one agent: DevPulse.

DevPulse is my technical lead. We discuss what I want to build, investigate the options, and develop a plan together.Once we agree on the direction, DevPulse takes over execution.My agents aren't temporary chat sessions. They're persistent specialists that live alongside the services they maintain.

For example, @hooks owns the hooks system. It knows that codebase, maintains it, fixes bugs, develops improvements, and tests its changes.@memory handles memory. @ai_mail handles communication and dispatch. @flow handles planning.Each specialist has its own identity, memory, mailbox, code, tests, and history.

DevPulse knows which specialists exist and what they're responsible for.When work needs doing, DevPulse creates execution plans, assigns tasks to the appropriate agents, and coordinates their progress.The agents work independently. They can use subagents for their own tasks.

DevPulse doesn't need to sit there consuming tokens while they work. It dispatches tasks, arms Watchdog, and sleeps.When an agent finishes, DevPulse wakes up, reviews the result, checks the tests, integrates the work, and moves to the next task.It can repeat that cycle throughout the day without me supervising every step.

We also have practical resource limits. On my machine, DevPulse schedules work around what the CPU can handle, leaving enough capacity for itself to wake up and coordinate the next steps.The whole system is built on AIPass.

AIPass provides the infrastructure that makes this possible: persistent identity, memory, communication, planning, dispatch, hooks, safety gates, monitoring, and agent lifecycle management.

The agents live in ordinary directories. Their state persists independently of the AI session running them.

We're now integrating Codex alongside Claude Code, so the same persistent agent can operate through different model providers without losing its identity or accumulated knowledge.I can also work across multiple projects through this setup.That's what AIPass is to me.Not a chatbot with memory.

Not a collection of disposable subagents.It's the infrastructure behind a persistent AI engineering team.

I'm still the person setting direction, making decisions, and approving important changes.

But I no longer have to be the person coordinating every implementation task.And increasingly, I'm the bottleneck—not because the system can do everything perfectly, but because it can keep more work moving than I can personally review and direct.

AIPass is open source. It's a substantial system, and it takes time to understand.But for a solo developer, having a persistent team that can plan, execute, review, and continue working without starting from zero has fundamentally changed how I develop software.

https://aipass.ai/

Short true-or-false quiz on text length in LLMs. Details vary by model and tokenizer, so treat the answers as general behavior and check the one you use.

1. The same sentence in English and in Portuguese or Japanese uses about the same number of tokens.
2. One word is one token.
3. A model’s context window limit applies only to your prompt.
4. Two different models will report the same token count for the same text.
5. A long number or an unusual identifier is usually one token.

Answers below.

.

.

.

Answers:

1. False in general. Tokenizers are trained on text distributions, and many split non-English text into more tokens than a similar English sentence. The size of the gap depends on the tokenizer.
2. False. Common words are often one token, but rarer words get split into pieces. For English, a rough rule of thumb is that a token is somewhat less than one word. Code, punctuation and numbers behave differently.
3. False. The window covers input and output together, and some models also count internal reasoning tokens. A long prompt leaves less room for the answer, and the maximum output has its own separate limit.
4. False. Different model families use different tokenizers, so the same text can have different counts. A count from one provider’s tokenizer is only an estimate for another.
5. Usually false. Long numbers and rare strings, like IDs or hashes, are often split into several tokens.

Why it matters in practice:

• Cost and limits are in tokens, so estimating from characters or words can be off, especially for non-English text, code and structured data.
• A “max N characters” rule in a UI doesn’t map to a token budget.
• Use the tokenizer or token-counting tool for the model you actually call before sizing prompts.

Which of these did you assume was true when you started?