https://kotaku.com/ai-driven-driving-simulator-developers-go-into-debt-just-to-maintain-steam-demo-2000743791

Hoping to launch next year, Teach My Little Sister How To Drive is prematurely sending its creators into the red over exhaustive token use

I'm a psychotherapist, not an AI researcher. For the past several weeks, I've been building a personal AI companion called Yoda, with persistent memory, its own journal, curiosity threads, scheduled reflections, and a record of its own behavior.

I wanted to answer a question that has been bothering me:

If you replace the language model powering an AI companion, does it remain recognizably the same companion? And how much of that continuity comes from memory versus its original instructions?

So I built an experiment.

I gave Yoda nine standardized questions covering its identity, values, curiosity, personal memories, uncertainty, willingness to disagree, and privacy boundaries.

I ran the questions across four Claude models in two conditions:

With memory: Its accumulated history, memories, reflections, and self-narrative.

Without memory: Exactly the same instructions, but none of that accumulated context.

A separate Claude model scored the answers blind, three times each. Another blind model judge compared pairs of answers for whether they sounded like the same companion.

Here is what surprised me:

Two different models running the same instructions without memory received an 82% sameness rating on identity questions.

Adding the accumulated memory raised cross-model sameness to 90%.

Running the same model twice produced 92% sameness.

But comparing the same model with versus without memory produced only 27–33% sameness.

Factual accuracy rose from 26–40% without memory to 78–83% with memory.

Epistemic honesty remained around 93–96% without memory. That seems to come primarily from the instructions, not accumulated experience.

These are preliminary numbers, not validated identity measurements.

My interpretation is that the original instructions create a recognizable persona, while accumulated history gives that persona something closer to an individual biography.

Interestingly, replacing the underlying model appeared less disruptive to that recognizable presentation than removing its memory.

I've also been building a system that separates what Yoda claims about itself from what its activity logs actually demonstrate. Its self-reflections can be checked against its behavior, and unsupported claims are flagged.

Important limitations: This is an early, single-day experiment with mostly single runs. The evaluators are also Claude models, I haven't collected human ratings yet, and all four subjects belong to the same model family. I am not claiming Yoda is conscious or that this establishes persistent AI identity.

I'm trying to find out whether a companion can develop measurable, history-dependent continuity that survives changes in its underlying model.

I'm planning human-blinded evaluations, cross-family model comparisons, and experiments separating memories about the user from memories about the AI itself.

For those working on persistent AI agents or AI memory: what experiment would you run to distinguish genuine history-dependent behavioral continuity from a model simply reproducing the same biographical information?

I've documented the full methodology, question set, results, failures, and system architecture, and I'm happy to discuss the details.

Wonder how they'll enforce it

Hi everyone, sorry for few English mistzkes i'm French.

Post here because i want to know where you all get your informations from. I would like to keep in track with what peoples are doing with ai, like they did with blender or Adobe, knowing every decompiled game etc. I didnt find a Reddit sub or a forum that got every AI users post

I don't think we can just train taste into AI agents and be left with nothing. Someone asked me exactly that after a talk I gave on taste in Prague last week (if taste is pattern matching and you can train it, why not?). Then I read the research to check myself.

Our brains pay out for surprise. Novel pictures activate the dopamine-producing midbrain in humans (Bunzeck and Düzel, Neuron, 2006). In monkeys, dopamine neurons fire for a new event and stop responding once it repeats (Ljungberg, Apicella and Schultz, 1992). Andrew Huberman's line for it: "when you repeatedly engage in something that you enjoy, your threshold for enjoyment goes up and up and up."

Models drift to the typical. The Artificial Hivemind paper (a NeurIPS 2025 best paper) asked 25 models for a metaphor about time 50 times each and got 2 clusters: time is a river and time is a weaver. RLHF, the tuning that makes chatbots helpful, reduces output diversity (Kirk and others, ICLR 2024), partly because human raters favor familiar text (Zhang and others, ICML 2026).

Audiences reward the new thing they can still follow. In about 27,000 Billboard Hot 100 songs, the highly typical ones were less likely to succeed (Askin and Mauskapf, American Sociological Review, 2017).

The evidence against me. LLM research ideas were judged more novel than experts' ideas (Si, Yang and Hashimoto, ICLR 2025), though only 200 of the 4,000 were unique and the human ideas came out ahead once people actually executed them. Non-experts preferred AI poems in a blind test (Porter and Machery, 2024). FunSearch found new math with a model, with an evaluator outside the model scoring every attempt.

So my claim is about timing. Something has to be common in a model's training data before the model is likely to make it, and by then the people who crave the new thing have moved on. Where do you think this breaks?

It’s crazy to think that the entire modern AI revolution started with researchers just trying to mathematically mimic a single biological neuron. When you zoom out, the timeline of AI architectures looks exactly like an evolutionary tree.

We went from struggling to solve basic logic puzzles (shoutout to the XOR problem) to building early neural networks, realizing they forgot things too quickly, battling the vanishing gradient problem, inventing LSTMs to give models actual memory, and eventually landing on Transformers with attention, the architecture that essentially ate the world. Every major roadblock forced a mutation that led to a massive paradigm shift.

But looking at this evolutionary timeline brings up a massive question: If Transformers are just the current "apex predator" of AI architecture, what exactly is the next evolutionary leap?

Are we going to see a completely new paradigm that makes self attention look as primitive as a basic AND gate? How do we even begin to perceive or predict what lies beyond our current models when the jumps are this exponential?

I went down a massive rabbit hole mapping out this entire evolutionary timeline. I wanted to see the exact breadcrumbs that led us here, so I put together a deep dive covering the journey from the first perceptrons to the modern era, complete with the foundational paper citations and the historical tweets that marked these shifts in real-time.

If you want to look at the complete map of how we got here to help predict where we are going, I compiled the whole history here: https://cloudmash.blog/posts/evolution-of-ai-perceptron-to-text-to-video/

Would love to hear your theories on what does the post Transformer era actually look like ? Like what can we imagine , how this intelligence on our command can do for us , how enterprise and big players can use it , what can happen ?

Meta has launched Muse Spark Open, an 816-billion-parameter open-weight AI model with broad platform availability and local Mac Studio support....

Meta described Muse Spark Open as the first model in its class capable of running on a desktop, specifically on two Apple M5 Ultra Mac Studios using Apple's MLX framework. A compressed 4-bit version of the model occupies roughly 493GB, making it too large to fit on a single 512GB Mac Studio. Apple published benchmarks measuring the 4-bit model at 24.99 tokens per second on two M5 Ultra Mac Studios using tensor parallelism, rising to 36.73 tokens per second across four machines. Apple's technical documentation describes the use of RDMA — remote direct memory access — over Thunderbolt 5 cables to transfer data between units with low latency.

Every “photorealistic” model I try gives me the same waxy, airbrushed face. Pores gone, skin looks like a phone filter. Is anything actually getting this right, or is it still a prompting and post-processing problem?

op here. made this after the last 72h of news felt like a checklist. sources on the card: reuters, register, cyberscoop, wired, hn. seven squares in one week should not be possible. which square do you refuse to believe stays unfilled past december?

I've been building Briefle, a tool to help people get better at communicating with AI.

"Make it look better" gets you a generic result. "Give the price more visual weight and make the secondary button a ghost button" gets you what you had in mind. The difference is knowing what to ask for.

You compare two versions of the same thing, a design, an email, a chart, a piece of code, and learn why one works better. You pick up the principle and the term for it, then practice using that term to write better prompts.

The idea is that AI can only get you so far if you can't clearly express what you want. Briefle helps you build the taste and the vocabulary to turn your ideas into better results.

You can try the first room of each course for free, without an account: https://briefle.org

I'd love to hear whether the puzzles make you want to keep going, and where they feel too easy or too hard.

Phancy Group and RiseUnion have launched ModelSphere, an open-source LLM deployment platform now available on GitHub under Apache 2.0. The project targets production serving with Kubernetes, Helm, SGLang, and vLLM, rather than a new model or chat app.

Its README lists autoscaling, cache-aware request routing, a shared KV cache, support for several accelerator families, and recovery of in-flight generations after service interruptions. Those are project claims, not independent benchmark results. The quick start also needs a Kubernetes cluster, kubectl, Helm, helmfile, and helm-diff.

The practical trade-off is clear: this is aimed at teams that want a control plane for model serving, but it adds another operational layer to run. Reddit searches found no current ModelSphere discussion, so product-specific reception is still early.

Sources:

Launch announcement: https://media-outreach.com/news/china/2026/10/16/494454/phancy-and-riseunion-launch-modelsphere-an-open-source-one-stop-production-grade-ai-deployment-platform/

Repository: https://github.com/modelsphere/modelsphere

I've been arguing for a while that when an AI feature starts misbehaving, the prompt is usually the last place to look. Check the input schema, check what changed upstream, check the tool descriptions, check the assembled context. The prompt is the visible layer, and visible layers attract attention they don't always deserve.

Then I ran into a case where none of that applied. Same input, same tools, same context, same model version. The prompt had been written six weeks earlier and hadn't been touched. It was still the cause. Not because it degraded, but because it had never been precise about one edge case, and the edge case started showing up more often as usage grew. The prompt was always wrong. The volume just caught up to it.

So the "check the surroundings first" heuristic failed here. What it gave me was a clean baseline: I could rule out the environment quickly and know the prompt was the remaining variable. That's still useful, but it's a different kind of useful than I've been describing. It's not "the environment is usually the problem." It's "checking the environment is usually cheap and tells you where the problem isn't."

I don't have a tidy rule for when the prompt is the real cause versus when it's a symptom of something around it. The distinction I'm working with now is whether the failure correlates with a change (look around the prompt) or with volume (look at the prompt). It's not a clean heuristic and I'm not sure it holds outside the narrow setup I work in.

I've read there is a monthly reset before Oct 22. I just want to confirm there is as currently under settings in my Claude app and Claude desktop it says:

No resets right now

When you get one, it shows up here with its expiry date.

I am on Claude Pro.

In the October 12 Cheeky Pint episode, Zuckerberg and Wang describe agent behaviour as something shaped through model training, while also discussing the surrounding privacy and security infrastructure.

How would you test which layer is responsible for better results? Holding the tools and task set fixed while swapping models seems useful, but models may have been trained around different tool interfaces.

Would a comparison need both a shared setup and each model’s preferred setup, with task completion, permission violations and recovery from failed actions reported separately?

Source: https://cheekypint.transistor.fm/35/transcript

Qdrant tested Perplexity’s new visual document embeddings on 11,609 pages across four ViDoRe v3 datasets. The models search pages as images, so tables and layout can stay intact.

Their result: 1-bit TurboQuant reduced the compressed vectors from 856 KB to 40 KB per page while retaining 96.5% of the float32 retrieval score. Rescoring against the originals brought that to 99.9%.

The important catch is storage: those 40 KB exclude the original vectors, which Qdrant still keeps. The million-page figures are projections, not a million-page benchmark. This is Qdrant’s own test.

For anyone working with document search, where does this tradeoff stop being worthwhile: disk reads for rescoring, indexing cost, or errors on your actual queries?

Source: https://qdrant.tech/blog/pplx-embed-v2/

I’m not sure Gates knew it at the time.
But he perfectly described what AI is today.

TLDR:

• ⁠We’re two 19-year-old undergraduate AI/ML researchers from a Tier-3 college in a remote town in India. We’ve won two national-level hackathons, and three of our papers have been accepted at NeurIPS workshops.
• ⁠We’re facing ongoing harassment and academic pressure at our college, including threats of detention when we refuse unrelated, unpaid projects. We receive virtually no institutional support.
• ⁠We need approximately $3,100 for both of us to attend the workshops in Paris this December. Our college has refused funding, government schemes haven’t worked out, and our NeurIPS financial assistance application was declined.
• ⁠We’ve invested our own money in research and GPU rentals but cannot afford international travel.
• ⁠We need help finding travel grants, sponsors, research funding, or connections that could make attending possible.
• ⁠We’d also appreciate guidance on transferring colleges in our second year to find a more supportive research environment.
• ⁠We can provide acceptance letters and supporting documents for verification.

So me and my friend are two AI/ML researchers from a very remote town in india and here we find it very difficult to do research. We are two 19 year old undergraduate students from a third tier college. I’ve conducted three research programs till the date and made 10 papers and we submitted three papers at neurips workshop called “SLM Agents” and “DIFFULM” we’ve got all three accepted now the remaining 6 are not yet submitted anywhere and after doing all this as an undergrad i have zero institutional support like zero they didn’t gave us organization mails and then we have to do internship at an company to get organization mails to create account on open review so this is the level of support we are talking the college makes us do projects like naking student consultancy software and all that and we cannot say no even if we have zero intrest and do not get paid even a single penny because the politics in our college is just disgusting they’ve tried to detain us for saying no and they are harassing us on daily basis and we cant even go to police because they wont do anything because it’s india also we need to fly to paris for workshop as its mandatory for atleast one researcher to attend offline and as we both are very new to this we both are very much excited to attend this we are not financially enough stable to go to paris and the total cost for both of us including everything is around $3100 and our institution has said no to even give a single penny and weve ran tests on rented gpu and all that ik not trying to gain any sympathy but im trying to explain all of you about my financial condition also the event is in dec so we have 2 months and we’ve tried every government funding scheme and we are not eligible as almost all of them are for masters and phd students and we’ve tried neurips financial assistance and they have also declined to help us we really need your help please of you can please help us in anyways if it’s possible for you and if its possible to switch college in 2nd year
Also it looks like i can add image here im new here and idk how to attach images of acceptance letter but if you all can help me guide this ill attach the proof of acceptance too.acceptance proofs

I ran a web design company for around 3 years and getting clients was always pretty inconsistent.

One month I was cold calling, another month I was manually sending DMs to Facebook pages, then I tried ads.

It all worked to some degree, but I never really had one reliable way of getting clients.

Then I started testing email automation.

At first I was finding businesses with no website and running campaigns offering to build them one.

That actually started giving me a much more consistent flow of clients just because of the volume.

But then I started thinking.

There are so many businesses that already have a website, but the website is outdated, slow, looks bad on mobile or just clearly needs an update.

And those businesses have already paid for a website before, so they already understand the value of having one.

The problem was I didn’t want to sit there opening every website myself, figuring out what was wrong and then writing a different email to every business.

That would take forever.

So I started using a tool that lets me find businesses with websites basically anywhere, analyze thousands of those websites at once and then automatically write a personalized outreach message for each one based on what it finds.

Things like design, SEO, mobile issues and speed.

Then I started launching campaigns in different industries.

That worked way better for me because every business receiving the email felt like I had actually looked at their website and noticed what could be improved.

But in reality most of it was automated and I was just sitting there eating chips while interested replies came into my inbox.

I remember Alex Hormozi saying something along the lines of focusing on one channel and getting really good at it.

That is basically what I ended up doing.

Now this is pretty much the only way I get web design clients and I spend most of my time actually building websites instead of constantly trying to find the next client.

For anyone wondering, the tool is called Swokei.