From: https://x.com/AndyMasley/status/1962196003521495256

Sources provided here: https://docs.google.com/spreadsheets/d/1nZCuvXG_uO1NTjr_VmHSfnMkEUm5DOA4DniHLsmB-40/edit?gid=0#gid=0

https://www.nbcnews.com/tech/tech-news/humans-hired-to-fix-ai-slop-rcna225969

Full summary:

AI was expected to replace creative workers, but instead it has created a new category of "AI cleanup" jobs where freelancers are hired to fix the mistakes and shortcomings of AI-generated content. From graphic designers correcting malformed logos to writers humanizing robotic text to developers debugging faulty AI code, creative professionals are finding work in addressing AI's limitations. Despite AI's growing presence, the market increasingly values the human touch, creativity, and quality that AI cannot fully replicate, suggesting that human workers remain essential for delivering polished, context-appropriate content.

TLDR summary;

This piece is framed in a particular way "fixing slop" but regardless, this is where the jobs are coming from with respect to AI - humans are the finishers.

Source:

https://werewolf.foaster.ai/

This is absolutely mind blowing. My mind cannot process that we went from copper tools to this in a couple thousand years. Hell, transistors are only like 75 years old.

To estimate AGI, I postulated that at AGI, we have: LLM size = Nb of neurons * Connections per neuron

Nb of human neurons = 100 billions Connections per neuron = 1000 So that: Nb of neurons * Connections per neuron = 100 trillions

LLM current size = 1 trillion

So in how much time will we reach a LLM with 100 trillion? Let's look at super calculators history, factor x100 every ten years

So LLM (2035) = 100 trillion And AGI in 2035

The problem, however, isn't the size of the UBI check, but rather whether that money retains any of it's value. $10k/mo is worthless if a gallon of milk costs $10k.

When will we see safe, scalable technologies that can truly boost human intelligence memory, reasoning, learning speed, creativity far beyond today’s limits?

Some possible paths I've considered:

• Somatic gene editing
• Advanced nootropic stacks
• High bandwidth brain computer interfaces
• Hybrid approaches

Questions for discussion:

• Do you think intelligence enhancement will first come from drugs, gene editing, or BCI?
• What’s the realistic upper bound for human intelligence?
• How should society regulate or democratize these tools?

Reaching Longevity Escape Velocity relatively soon is contingent on Artificial Super Intelligence existing first. Ultimately when someone gives a date for when LEV is going to be achieved (and they are aware of the wider metaphenonenon of accelerating returns within technology as a whole, as well as AI) they likely shouldn't be placing the advent of LEV to far after the emergence of super-intelligence. So, when do you guys think something approximating super-intelligence will be achieved? I personally think that something approximating this will arrive in 2029-2030, at the earliest. I don't think it will arrive later than 2035, so I think LEV could be as soon as 2034-2040. What do you guys think? What's your reasoning?

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

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?

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?

• 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