My dream for FDVR is essentially super advanced Genie + VR + IRL haptics. History can be revisited, or better yet, history can be changed by your actions...

What historical events would you re-live in FDVR?

Guys did u see meta’s deepconf it got 99.99% on aime by using confidence factors for gpt oss

The question isn’t if. It’s how fast.

https://copilot.microsoft.com/labs/audio-expression

Pick Moss for voice and for style shyness, sadness or joy , prompt "Hi, I'm Samantha, your new friend, im here to support you " and hit generate ... let me know if its her :)

It will be available at the end of this year, iirc

GPT-5 is optimised for ~1B users, so it is not the most powerful model technically possible. Also remember there is IMO gold model behind the scenes. Considering these factors and ongoing exponential trend, how powerful could be such costly model in November-Desember 2025? I think it will be as satisfying as o3 in January

Tech shifts often feel gradual, but then suddenly something just vanishes. Fax machines, landlines, VHS tapes — all were normal and then gone.

Looking ahead 20 years, what’s around us now that you think will completely disappear? Cars as we know them? Physical cash? Plastic credit cards? Traditional universities?

The link is a substack article by Ethan Mollick (A professor at the Wharton School of the University of Pennsylvania). Opening paragraph below:

"More than a billion people use AI chatbots regularly. ChatGPT has over 700 million weekly users. Gemini and other leading AIs add hundreds of millions more. In my posts, I often focus on the advances that AI is making (for example, in the past few weeks, both OpenAI and Google AIs chatbots got gold medals in the International Math Olympiad), but that obscures a broader shift that's been building: we're entering an era of Mass Intelligence, where powerful AI is becoming as accessible as a Google search."

• OpenAI launched a $50M People-First AI Fund to support U.S.-based nonprofits and community organizations, with applications open from Sept 8 to Oct 8, 2025. The grants aim to foster innovation and resilience, especially in areas like education, healthcare, and economic opportunity, with a focus on creative uses of AI. https://openai.com/index/supporting-nonprofit-and-community-innovation/
• OpenAI GA’d the Realtime API and introduced gpt-realtime (speech-to-speech) with MCP server support, image input, SIP calling, reusable prompts, async function calls, context controls, and two new voices (Cedar, Marin); internal evals: Big Bench Audio 82.8%, MultiChallenge 30.5%, ComplexFuncBench 66.5%; pricing cut ~20% to $32/1M audio input tokens ($0.40 cached) and $64/1M audio output; EU data residency and safety guardrails. https://openai.com/index/introducing-gpt-realtime/
• Anthropic is adding a revocable opt-in that lets chats and Claude Code from Free/Pro/Max accounts train new LMs and extends retention from 30 days to 5 years for opted-in sessions, applying only to new or resumed activity; Work, Gov, Education, and API traffic stay excluded. Users must pick a setting by September 28, 2025 to continue; you can change it anytime, and if you later turn it off, Anthropic stops using future data but cannot pull your data from models already trained or runs already underway. https://www.anthropic.com/news/updates-to-our-consumer-terms; https://www.anthropic.com/legal/non-user-privacy-policy
• Microsoft released two in-house models: MAI-Voice-1, a high-fidelity, multi-speaker TTS that generates ~60 s of audio in <1 s on a single GPU, now powering Copilot Daily and Podcasts and available in Copilot Labs; and MAI-1-preview, an instruction-following MoE foundation LM trained end-to-end and post-trained across ~15,000 NVIDIA H100s, now live for public eval on LMArena, with limited API access for trusted testers and near-term Copilot text deployments. Voice-1 targets expressive narration and dialogue; the preview LM focuses on helpful, aligned responses, with rapid iteration planned through user feedback. MAI emphasizes a product strategy that orchestrates multiple specialized models, not a single monolith, mixing in-house, partner, and open-source systems. The org’s next-gen GB200 cluster is operational, signaling aggressive scaling beyond H100 and a pipeline for larger, faster updates. https://microsoft.ai/news/two-new-in-house-models/
• xAI released grok-code-fast-1 a fast, low-cost reasoning LM for agentic coding, built from a new architecture with programming-heavy pretraining and post-training on real PRs, and it natively drives grep, terminal, and file edits in IDEs. Serving is tuned for low-latency tool loops with >90% prompt-cache hit rates in partner integrations, yielding a feel where dozens of tools fire before you finish the first paragraph of the thinking trace. It is strong across TS, Python, Java, Rust, C++, and Go, handling zero-to-one builds, codebase Q&A, and surgical bug fixes with minimal oversight. Availability: free for a limited time on GitHub Copilot, Cursor, Cline, Roo Code, Kilo Code, opencode, and Windsurf; API pricing is $0.20 per 1M input, $1.50 per 1M output, $0.02 per 1M cached input. Reported results include 70.8% on SWE-Bench-Verified via an internal harness, a stealth rollout as “sonic” with multiple checkpoints, and a near-term variant in training for multimodal inputs, parallel tool calling, and longer context; if these hold in real IDE loops, iteration time collapses and agentic coding trends toward default-grade automation. https://x.ai/news/grok-code-fast-1
• AI2 released OLMoASR, a fully open ASR family (39M–1.5B params) trained from scratch on a curated 1M-hour dataset distilled from a 3M-hour pool, with every layer—data, filtering code, model weights, and evaluation—public. Across 21 unseen short- and long-form tests, the models match or nearly match Whisper’s zero-shot WER (e.g., OLMoASR-medium ≈ Whisper-medium; large-v2 closes the gap to ~0.4%), highlighting data curation as the main driver and providing a reproducible platform for ASR research. https://allenai.org/blog/olmoasr; models: https://huggingface.co/allenai/OLMoASR; code: https://github.com/allenai/OLMoASR
• Apple (holy hell Apple releasing a PAPER?) | MobileCLIP2: Improving Multi-Modal Reinforced Training - MobileCLIP2 upgrades multi-modal reinforced training end to end: swap the base to DFN, replace OpenAI+DataComp teachers with a tuned DFN ensemble (ViT-L/14 + s39b) using per-teacher temperature for contrastive KD, pretrain CoCa on DFN-2B then fine-tune on MSCOCO-38k (plus ablate DOCCI/GBC/DCI) to boost caption diversity without hurting robustness, and pack the reinforced DFNDR datasets with 30 image augmentations and 5 captions per image so offline distillation stays compute-flat but 3.3–5× more sample-efficient than prior DataComp/DFN baselines and up to 1.7× at 13B seen. Architecture-wise, new 5-stage FastViT encoders (MCi3/4) shift heavy ops deeper to shrink latency at higher input resolutions and fill the speed/size gap between S2 and L; beam search and longer caption contexts bring no gain, while mixing captions from multiple captioners yields only additive but small improvements. Results: MobileCLIP2-S4 hits SigLIP-SO400M/14 zero-shot on IN-1k at half the parameters and outruns DFN ViT-L/14 at 2.5× lower latency; MobileCLIP2-B adds 2.2% IN-1k over MobileCLIP-B; S0/S2 set SoTA in the 3–7 ms regimes. Released code and scalable DR tooling make spinning new teacher ensembles and datasets trivial, pushing on-device VLM toward ubiquitous, low-latency intelligence without ceding accuracy. https://arxiv.org/abs/2508.20691; models: https://huggingface.co/collections/apple/mobileclip2-68ac947dcb035c54bcd20c47
• StepFun released Step-Audio 2 it’s a SoTA end-to-end audio LM that ingests raw speech and emits interleaved text+audio tokens, coupling a frozen 25 Hz encoder with a 2× adaptor to 12.5 Hz, a CosyVoice 2 tokenizer (+6.6k audio tokens), and a flow-matching detokenizer with HiFi-GAN; history is prefilled for streaming, and external tools include web, weather, time, and a large audio search for timbre/style retrieval. Training stacks 1.356T tokens over 21 days: 100B ASR to align the adaptor, then 128B text + 128B audio to embed audio tokens, then 800B mixed data spanning ASR, TTS, S2TT, S2ST, continuations, and speech conversation, then a 200B cooldown with multilingual ASR, paralinguistics, and synthetic dialogues across ~50k speakers. SFT adds 4B tokens over curated ASR, AudioSet/AudioCaps QA, detailed paralinguistic captioning, CoVoST2 and CVSS pairs, scripted tool-call dialogues, and conversation synthesis. RL sharpens reasoning via two-stage PPO that rewards concise thinking, then learned preference scoring, followed by 400-iteration GRPO; actor lr 1e−6, critic lr 2.5e−6, batch 64. Results: SoTA or parity on ASR, paralinguistics (StepEval-Audio-Paralinguistic), audio understanding (MMAU), zh↔en S2TT and S2ST, tool calling (StepEval-Audio-Toolcall), and URO-Bench speech conversation. Step-Audio 2 mini (8.32B, Apache 2.0), initialized from Qwen2.5-7B with the Qwen2-Audio encoder, reproduces most gains with only web tool support and is available with scripts for local and realtime demos. This design proves that fully interleaved token generation plus retrieval-equipped tooling and RL can unlock low-latency, expressive, knowledge-grounded voice agents that scale with data and crush legacy cascades. https://arxiv.org/abs/2507.16632; Models: https://huggingface.co/collections/stepfun-ai/step-audio-2-68b003c3a47b273fffaf67a8

let me know if I missed anything

This is either a massive troll… or history in the making.

https://x.com/bengoertzel/status/1958528193477898552

As we get closer and closer to Singularity many things are going to be reeling back and forth more and more rapidly and crazily.

Opinions on AI and AGI will likely be among these things to perk around...

Remember last fall there was a big media narrative of AI running out of steam...

Then came reasoning models... which have had a nice run... then came DeepSeek... etc

Now we have a brief bout of AI gloom and doom narrative again... and within months at most, I predict, another breakthrough will be launched to turn the narrative around...

Those who succeed most will be the ones who clearly see the broader Kurzweilian trend and don't get preoccupied with the momentary model release or memetic trend...

The excitement and innovation at the recent AGI-25 research conference at U. Reykjavik was incredible... and now I am heading to an AGI hackathon in Nairobi... the push toward AGI is global...

This is not to say stocks won't rise and fall, nor that LLMs won't fade into a role as valuable components technologies....

@GaryMarcus is right that LLMs and DNNs altogether are overhyped and some LLM valuations will suffer corrections... and that neural-symbolic methods deserve way more love, attention and funding.

But @sama is right that human level AGI is near and yes will soon merit trillions of $$ in hardware investment

Very wise words from Goertzel. The question is, what is next after the reasoning models and how far away is it?

I've been building something recently and wanted to get some honest feedback.

The idea: • You give an Al ongoing context about what you're working on, building, or thinking about. • Instead of having to re-explain everything each time, the Al already knows the background and can respond in a more useful way. • You can also share that same context with other people, so when you're collaborating, they don't just see the end result, but the thought process and progress behind it.

So it becomes like a portable memory layer: Al remembers your projects, and humans can plug into that same memory without long explanations.

Kind of like moving from one-off conversations → to a shared workspace of thoughts + reasoning.

• Would you actually use this? • If yes, where would it be most useful (personal productivity, team collaboration, creative projects, etc.)? • If no, what's the biggest blocker?

They have what appears to be the best AI image editor on the market by far.....and decided to make it utterly worthless with insanely aggressive censorship that just appears to censor everything as far as I can tell. Even with prompt engineering I can only get it to do anything at all about 10% of the time. With totally safe-for-work images. I am done with it, the juice is not worth the squeeze.

Oh, and for good measure it adds a watermark....the effect of which is just to make me fucking waste 20 seconds removing it in any one of a million other AI editing tools so its just a totally pointless nuisance.

Classic google. If OpenAI releases an updated chatGPT image gen then the vaunted "nano banana" will be another forgotten google release consigned to the dustbin of tech history

Some incredibly exciting news from the world of protein engineering. A new paper in Nature introduces BindCraft, an open-source and automated pipeline that's poised to change de novo protein binder design forever.

For anyone who's ever worked with protein-protein interactions, you know how complex and challenging it can be to design binders from scratch. It's often a painstaking process with low success rates.

But BindCraft reports experimental success rates of 10-100%!!!

BindCraft uses the learned "knowledge" (weights) of AlphaFold2 to generate binders. This means it can predict and design high-affinity binders without the need for traditional high-throughput screening or experimental optimization.

This is huge, because they're moving towards a paradigm where computational design can directly yield effective binders, even against challenging targets and without pre-existing binding site information.

They've already successfully designed binders against a diverse range of tough targets, including Cell-surface receptors, Common allergens (like reducing IgE binding to birch allergen), de novo designed proteins, and Multi-domain nucleases like CRISPR-Cas9 (they can modulate its activity).

An incremental improvement this is not. Its a fundamental shift in how we can approach protein engineering. The potential for therapeutics, diagnostics, and biotechnology is absolutely enormous.

From The Nature Paper:

"Protein–protein interactions are at the core of all key biological processes. However, the complexity of the structural features that determine protein–protein interactions makes their design challenging.

Here we present BindCraft, an open-source and automated pipeline for de novo protein binder design with experimental success rates of 10–100%. BindCraft leverages the weights of AlphaFold2 (ref. 1) to generate binders with nanomolar affinity without the need for high-throughput screening or experimental optimization, even in the absence of known binding sites.

We successfully designed binders against a diverse set of challenging targets, including cell-surface receptors, common allergens, de novo designed proteins and multi-domain nucleases, such as CRISPR–Cas9.

We showcase the functional and therapeutic potential of designed binders by reducing IgE binding to birch allergen in patient-derived samples, modulating Cas9 gene editing activity and reducing the cytotoxicity of a foodborne bacterial enterotoxin.

Last, we use cell-surface-receptor-specific binders to redirect adeno-associated virus capsids for targeted gene delivery.

This work represents a significant advancement towards a ‘one design-one binder’ approach in computational design, with immense potential in therapeutics, diagnostics and biotechnology."

I had three different slide decks to do today to vulgarize R&D projects for students / teachers who collaborate with my workplace. And ChatGPT's Agent Mode helped me breeze through all of them in one afternoon. Little soldier will web search details, rip out pictures from web pages or PDFs, draw its own pictures when it feels fancy, all of its own volition.

Current LLMs are intelligent and self-aware enough to:

• Understand instructions;
• Plan actions and follow through based on these instructions;
• Identify and correct mistakes.

Is the power point going to be perfect? No, I'll tweak it. Is it saving me an hour every time? You bet. (And I am very productively wasting that time to wander on Reddit, don't tell).

I'm routinely flabbergasted by the literal autonomous magic current AI can achieve. And yet I still see masses going "AI is not useful / not revolutionary / hitting a wall". All I can conclude is the average person is still not even aware of what black magic is currently available to them.