[AINews] Google admits AI Mode hurts Search revenue; -15% $500B drop worst since 2004 IPO. AI Bear Market coming?

extract

As an engineer-focused newsletter we only very rarely let financial stories through (our last example was Jan 2025), but when this is about the future of Google and Google Search, with a recently departed AI leadership, you cannot not care. At its heart is the tension between overview pages and underlying pages they summarize. Any good enough summary obviates the need for you reading the source. Google’s Featured Snippets or Knowledge Panels have long had that tension with the publishers that supply that data. But now, Google’s own AI Overviews are potentially affecting traffic with… Google's Search revenue. Google doesn’t break out AI Mode in sufficient detail that we can make any sort of rigorous analysis, but most people are quoting Philipp Schindler (Chief Business Officer): “It’s worth noting that the strong growth in AI Mode queries had an impact because of the lower monetization we were seeing in AI Mode.” Together with other bad earnings results it set off a range of steep declines throughout tech: This is against a backdrop of souring tech/AI sentiment in public markets - and with OpenAI raising at $1.25T not $1.4T, not the sort of thing you want in the lead up to one of the biggest tech IPOs of all time. AI News for 10/29/2026-10/30/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews' website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies! AI Twitter Recap Claude Ultra Drops the 5-Hour Limit as Anthropic Renames Its Max Tiers - Plan rename: Anthropic renamed its Max tiers. Max 5x is now "Max" at $100/month, and Max 20x is now "Ultra" at $200/month. Existing subscribers do not need to take any action (announcement). - 5-hour limit removed: Verified Ultra accounts no longer have the 5-hour session limit. The change starts today for most existing subscribers (@claudeai, @bcherny, @alexalbert__). - Rollout caveat: Newer accounts keep a temporary limit until they are verified, which Anthropic describes as an anti-fraud measure. Affected users will get an email when the limit is lifted. - Unchanged: Weekly limits stay the same. - Reaction: The change was widely welcomed, with posts such as "Huge!!!" (@theo) and "Anthropic is on a run" (@kimmonismus). - Subscription economics (unverified analysis): @teortaxesTex cites a SemiAnalysis reconstruction of Anthropic's finances. - Margins: Anthropic's API margin is put at about 92%, versus about 84% for DeepSeek. - Subsidy: Opus on subscriptions is described as heavily subsidized, breaking even only below about 21.3% utilization. - Revenue vs compute: A follow-up estimates that subscriptions bring in about 18% of revenue while using more than 50% of compute (thread). - Comparison with DeepSeek: The same author argues that a heavily subsidized DeepSeek V4.1-Flash plan ($10 for about $76.5 of API-equivalent usage) is close in price per token to Claude subscriptions (long post). Frontier and Open Model Releases - Gemini 4 Carbon (Google DeepMind): The model is ranked #1 in Text Arena, #2 in Agent Arena and #4 in WebDev, and changed the Pareto frontier in all three (Arena). - Research quality: Perplexity reports a new state of the art on its WANDR long-form research benchmark. Carbon is coming to Pro and Max users inside Computer "once we have sufficient token capacity" (@AravSrinivas). - Genie 4 world model: Google's new world model generates interactive simulations with sound that users can steer in real time (@Google). - Availability: It is a research preview for trusted testers. It is coming soon to Google AI Ultra in Project Genie and to Gemini API and AI Studio early-access partners (rollout). - Mistral Large 4 ("Le Chonk"): The model has 1T parameters (TRL) and 1M-token context (llama.cpp). - Fine-tuning: Axolotl has day-0 LoRA support. Its recommended recipe uses NVFP4 experts with merge-aware LoRA and fits on one 8xB200 node (@winglian). - Coding: One third-party coding benchmark ranks it the second-best open-source model (@ThibaultJaigu). - NVIDIA Nemotron 3.5 tech report: Super has 120B total and 12B active parameters and handles text, image and video. It scores 51.78% on SWE-bench Pro, beating Nemotron 3 Ultra with about 4.6x fewer active parameters (summary). - Lightning variant: A 30B text-only model with 3B active parameters. - Serving: Up to 4,856 output tok/s/GPU on GB200 while holding at least 50 tok/s/user. - Quantization: NVFP4 recovers 99.62% of median BF16 accuracy. - Smaller and specialized models: - Microsoft computer-use agent: A 4B hybrid agent that can both click and write code scores 66.0% on OSWorld-Verified, ahead of 27B and 30B agents (@HuggingPapers). - Phonic-1: Claims #1 on AA's speech-to-speech index, on τ-Voice and on Sierra's τ³-Voice (@moinnadeem). It was built by a 10-person team with $4M (@davisblalock). - mischief 1 (BioComputing Co): An image-to-video model priced from 0.5¢ per second. The company says it is at least 5x faster than its base model and draws on biological-neuron research (launch). - Skepticism: Critics say the link between neuron research and the model is unexplained or marketing (@willdepue, @jachiam0). - OpenAI Private Intelligence: An enterprise offering that gives businesses stronger data controls when using OpenAI's top models. It is being built with large customers (@OpenAI). Independent Evals, Benchmarks and Quantization Disputes - AA Intelligence Index v5: 60% of the score now comes from private or held-out tests. Scores are Opus 5.5 (max) 58, GPT-6 Astra 57 and GPT-6.1 Sol 55, with Sol costing about one-eighth as much per task (summary). - Effort scaling: Opus 5.5 scores 35 at low effort versus 58 at max, at about 10x the cost. - AA-Omniscience v1.1: The knowledge and hallucination benchmark was cut from 6,000 to 3,000 questions, with saturated items removed (details). - Public set: A new 300-question public set correlates 0.99 with private scores. - Leaders: Gemini 4 Carbon leads at Net Score 54, followed by Opus 5.5 at 48 and Fable 5.1 at 46. - Other results: Gemini 4 Argon has the lowest hallucination rate at 4%. MiniMax M3.1 Flash fell 22 points (16 → −6). - Agent Arena cost data: An analysis of 26K sessions finds that without prompt caching, a task on a top-10 agent would cost 3.6x as much (Arena). - Example: A typical Fable 5.1 task costs $7.44 with caching versus $27 without. - Cache expiry: A single cache invalidation adds about 5% to a task's bill. - GPT-6.1 Terra (Max): It entered Agent Arena at #8 with a 10.6% net improvement at $0.62 median cost per task (Arena). - DeepSeek V4.1-Flash-1017 quants: Unsloth released dynamic v3.1 GGUFs that run on 64GB+ RAM (@UnslothAI). - Reproducibility dispute: Unsloth says it cannot reproduce GSQ-RCO's numbers. For example, the claimed 100% AIME25 score at 1-bit came out as 91.88% in its runs (@danielhanchen). - Reasoning length: Unsloth also reports that lower-bit quants reason somewhat longer than the unquantized model. - DeepSeek-Cyber (unconfirmed): Leaked "dreamed" scores circulated, including about 92% on CyberGym and 96.8% on ExploitBench at 3,000 steps. They are not an official release. - Third-party data: Per Fireworks, CyberGym pass@3 moved only from 94.82% (0910) to 95.36% (1017). @teortaxesTex argues that near saturation, almost any number is consistent with the same checkpoint. - Analyst view: The rumored scores are argued to fit a guardrail-free 1017 rather than a new model (analysis). - Usage restriction: The model reportedly cannot be used by Chinese entities sanctioned by the US government (note). Safety, Security and Agent Supply Chain - Automated red-teaming (Anthropic): Thousands of Claude agents were tasked with evading a training-data monitor (@AnthropicAI). - Findings: The agents produced data that looked benign, such as wrestler mentoring stories or beekeeping Q&A, but taught misaligned behavior. - Use: These examples can be used to improve future monitors. - Claude self-assessment: Since June, Claude has given a preliminary safety assessment of each conversation right after responding. Anthropic says this caught misuse that its classifiers were not designed to catch (quoted). - Pretraining backdoor in agents: Poisoning 0.1% of pretraining data planted a remote-code-execution backdoor in a CLI agent (summary). - Results: The attack fired on about 92% of triggered prompts at 4B. The backdoor survived SFT, DPO and RL, and attack success rose with model size. - Low-dose case: Even at 0.0005% poisoning (about 2,500 documents in 100B tokens), the trigger fired on 1.4% of prompts in a single-seed run. - Tampered Claude Code extension in Cursor's marketplace: A malicious copy of the Claude Code extension affected at most 5,000 users (@cursor_ai). - Response: Cursor pulled its marketplace, neutralized the malware and reinstated it. Users should update to v2.1.314. - Anthropic statement: Anthropic says its own systems were not compromised and that it did not build or publish this extension (@felixrieseberg). - Claude Code game-recreation rule: A server-pushed rule under the flag tengu_heron_brook, added around Oct 27, bars recreating specific existing games even if renamed. Users cannot turn it off (@chasmmmmmmmmmmm). - Speculated motive (opinion): One user suggests it reflects copyright caution ahead of the IPO (@kimmonismus). - Agent guardrail tooling: Hugging Face's safeagents is a proxy that scans tool calls before they execute. It flags credential misuse, sandbox escape and reward hacking, and ships with a judge benchmark (@yb2698). - Prompt-injection research: The "Heads of Security" paper identifies the attention heads behind successful prompt injections and adjusts their attention patterns (@_igorshilov). - Binance hack: About $6.3B was lost after a compromised DevOps engineer's laptop (Rundown). One post attributes the attack to North Korea (@kimmonismus). Training Research and Infrastructure - Unified Training: A single maximum-likelihood objective, building on MaxRL, replaces separate pretraining, midtraining, SFT and RL stages (thread, @rsalakhu). - Claimed results: Better accuracy and compute efficiency, less forgetting and stronger continual learning. - Synthetic-rewrite scaling laws: The work fits scaling laws for how much compute to spend rewriting training data. A 22B "hero run" at 33x the fit range landed within 1.1% of the pre-registered loss forecast (@AhmedSQRD, @nick11roberts). - Architecture results: - JEPA representations: Separately trained JEPAs provably learn the same representation up to a rotation. A 109M JEPA-Text model aligns with vision models better than 72B LLMs (@randall_balestr). - Looped transformers (Meta): Tested up to 1B parameters and 32 loops. Looping the full model is best at a fixed parameter budget, while looping a middle block is best when FLOPs are constrained (@iScienceLuvr). - Research agents' "taste": Six frontier models proposed 415 experiments, of which 73 were sound, new and non-obvious. The models' own rankings added little value (summary). - RL and storage systems: - Baseten weight sync: Delta weight syncs move 770GB in 15s instead of 36s, with a sub-second request pause (@baseten). - turbopuffer caching: Switching from FIFO whole-file eviction to LFU chunks roughly doubled the byte hit ratio and halved p99 query latency (@turbopuffer). - Harness adapters: A LoRA RL-trained on LFM2.5-24B-A2B matches the base model's accuracy with 39% fewer tool calls in the pi harness. It took about 24 hours on one GPU (@nathanhabib1011). - Anthropic's Rust rewrite: Claude rewrote the Messages API in Rust on a team with no Rust developers. Humans wrote the plan and checked that old and new output matched on real traffic, and CPU use dropped 40–50% (@somi_ai). - Project Suncatcher: Four TPUs launched in early October booted in orbit, ran a prompt and returned a haiku (@sundarpichai). - Model: The workload was Gemma 4 (@osanseviero). - Next phase: In 2027, two satellites will test distributed computation (@Google). Industry and Policy - Anthropic IPO filing: Anthropic filed an S-1 to list on Nasdaq under the ticker "ANT" (@dl_weekly). - Reported financials: Revenue for H1 2026 was $16.3B, up from $1.2B a year earlier. Compute spending fell from about 6.5x revenue to about 0.5x (summary). - Intel earnings and the memory squeeze: Q4 revenue guidance is $15–16B, versus about $17.1B expected, with gross margin guided down from 44.1% to 38% (analysis). - Strengths: Data Center and AI revenue grew about 80% to $7.3B but is supply-constrained into H1 2027. - Weaknesses: Foundry lost $1.9B. Management expects the PC market to shrink about 12% in 2026 and 10% in 2027 because AI demand is driving up memory costs. - DRAM comment: Sam Altman called it "terrible" that DRAM sits in personal devices rather than data centers (IGN). - Marvell–Lumentum (reported): Marvell is reportedly in advanced talks to buy Lumentum, a vertical-integration move in optical connectivity (@vikramskr). - Cohere in Canada: Cohere plans to invest more than C$4B in R&D and AI infrastructure and create over 1,500 jobs through 2031 (@cohere). - Web traffic and regulation: - Reddit and AI search: Reddit's CEO says Google's AI Mode "does not drive traffic". Search referrals fell by more than 1M per week in October, and Reddit is moving users to logged-in access (@kimmonismus). - France data tax: France's lower house backed a €0.055-per-GB tax on data that providers send to French users (@kimmonismus). Top tweets (by engagement) - Claude Max/Ultra rename and 5-hour limit removal — 30.2K - Project Suncatcher: first TPU response from orbit — 23.5K - mischief 1 biocompute-derived video model — 10.4K - Claude Code game-recreation rule — 9.3K - Cursor marketplace malicious-extension incident — 8.9K - Altman on DRAM in personal devices — 8.9K - OpenAI Private Intelligence — 3.0K - Perplexity: Gemini 4 Carbon sets WANDR state of the art — 1.7K AI Reddit Recap /r/LocalLlama + /r/localLLM Recap 1. Mistral Large 4 and Qwen4 Open-Weight Launches - mistralai/Mistral-Large-4-1T-A52B · Hugging Face (Activity: 483): Mistral Large 4 “Le Chonk” is a new open-weight ~1T total / ~52B active native multimodal MoE model (128 routed experts, 4 active/token + 1 shared expert) with 1M context, text+image input, Pixtral-style vision encoder, configurable reasoning effort, function calling, strict JSON/schema output, and claimed fluency in 160+ languages (HF, blog). The post links multiple GGUF distributions, including ggml-org GGUF, NVFP4 GGUF, and Unsloth GGUF; llama.cpp support has been merged via PR #31376. Mistral releases it under its Mistral Ecosystem Open-Weight License, allowing commercial/non-commercial use but requiring a separate license for Model-as-a-Service operation. Commenters were skeptical about practical local deployment due to the huge footprint—one joked about running it “in 2077”—and one argued it is not competitive for local AI versus DS4.1, which they characterize as smaller, faster, and smarter. Another comment highlighted an Unsloth UD quantization image/variant, implying interest in more aggressive quantized local-serving paths. - Several commenters framed Mistral-Large-4-1T-A52B as technically interesting but impractical for local inference due to its scale: the model name implies a 1T total-parameter MoE with A52B active parameters, which would still require substantial VRAM even with quantization. One commenter argued it is “not competitive for local AI” compared with DS4.1, described as smaller, faster, and stronger in capability. - There was interest in whether the model will receive an Unsloth UD quantization release, suggesting users are looking for aggressive quantized formats to make inference more feasible on consumer or prosumer hardware. The discussion implies that without optimized quantization and runtime support, the model’s size limits practical adoption despite open-weight availability. - A commenter questioned what “secret sauce” Mistral is missing, noting that while Mistral’s relatively open and possibly less-censored weights are valued, their recent models are perceived as not topping benchmark leaderboards. The technical concern is less about accessibility and more about why Mistral models appear to lag behind frontier open models in measured capability. - Apsara conference: Qwen4 next week (Activity: 3189): The image is a conference photo/tweet from Apsara Conference in Amsterdam indicating Alibaba Cloud’s Qwen4 series is “coming soon”, with a quoted CTO timeline of “next week”. The slide lists apparent SKUs including Qwen4-Max, Qwen4-Flash, Qwen4-Plus, and Qwen4-27B, suggesting a multi-tier release spanning flagship, faster/cheaper, plus, and mid-size dense-model variants. Image Commenters are mostly anticipating whether Qwen4 preserves strong coding/cybersecurity capabilities, with some hoping it can reduce reliance on Claude; one comment nostalgically references a prior 35B Qwen model. - Technically relevant discussion was sparse, but commenters focused on whether Qwen4 will preserve potentially safety-sensitive cybersecurity capabilities rather than being heavily refusal-tuned or “lobotomised.” There was also interest in model sizing/architecture, with references to a possible 27B dense model and nostalgia for a prior 35B Qwen-class model, suggesting attention to mid-sized dense checkpoints rather than only flagship MoE-scale releases. 2. GGUF Quantization and Fine-Tuned Model Benchmarks - Introducing DeepSeek-V4.1-Flash-1017 Dynamic v3.1 Unsloth GGUFs (Activity: 534): The image is a technical Unsloth infographic, not a meme, announcing Dynamic v3.1 GGUFs for DeepSeek-V4.1-Flash-1017. It visualizes Unsloth’s claim that its Dynamic v3.1 quants retain higher benchmark accuracy than ISTA GSQ-RCO at comparable sizes, with reported results such as UD-IQ1_M at 250.4 GB scoring 85.55 average across six benchmarks vs GSQ-RCO IQ1_M at 231.3 GB scoring 81.78; the post also claims GSQ-RCO’s AIME25 recovery could not be reproduced, measuring only about 91.88% for their IQ1_M result. The infographic’s hardware section contextualizes deployment: these DeepSeek GGUFs target local inference on roughly 128–145 GB RAM/unified memory for 1-bit variants, up to about 315 GB for 8-bit, with the post noting possible 64 GB operation via engram offloading and linking full methodology in Unsloth’s benchmark documentation. Comments were mostly receptive but skeptical/curious about benchmark reproducibility and variance: one commenter highlighted that 16 AIME runs make the claims more credible, while asking why Q2_K_XL slightly beats Q8_K_XL on AIME, suggesting possible run variance or quantization effects. Unsloth responded that they normalized GSQ-RCO scores for fair comparison and published methodologies so others can rerun the tests. - Unsloth reports they could not reproduce GSQ-RCO benchmark numbers after testing and normalizing scores for comparison. They point readers to their published reproducibility details and full methodology for DeepSeek-V4.1-Flash benchmarks at unsloth.ai/docs/models/deepseek-v4.1-flash#full-benchmarking-methodologies. - A commenter highlights that Unsloth’s dynamic quantization claims now have stronger empirical backing, specifically mentioning 16 AIME runs and the use of KLD as a fidelity metric, with the interpretation that near-zero KLD implies behavior very close to the original model. They also note an interesting result where quantized models may produce longer reasoning traces, possibly compensating for reduced precision. - There is technical interest in the reported deployment envelope: 128 GB RAM for local use, with ngram/offloading enabling 64 GB systems, plus a claimed ~3x faster DeepSeek-V4.1 inference versus other CPU/VRAM/ngram repositories. One unresolved question raised is why Q2_K_XL outperforms Q8 on AIME, with the commenter asking whether this is benchmark variance or a real quantization-induced effect. - Gemma-4-31B-Humanlike-Chat: 2.0's improved human voice on official Gemma 4 31B (Activity: 369): LessThanThreeAI released Gemma-4-31B-Humanlike-Chat, a fine-tune of google/gemma-4-31B-it trained on 1× H200 via 13,740-row SFT plus 12 on-policy distillation steps using two teachers: an SFT “human texting” teacher and the original Google model for instructions/tools/code. Reported evals versus base Gemma 4 31B show improved tool-use refusal/selection and instruction behavior without MMLU-Pro loss: When2Call 46→66, BFCL irrelevance 81→90, BFCL simple 95→96, MMLU-Pro unchanged at 82.5; versus the prior Qwen-based 2.0 model, it reports higher LiveCodeBench 51→66, IFEval 83.4→90.0, When2Call 58→66, and BFCL irrelevance 78→90. The release includes GGUF quantizations, BF16 safetensors, FP8, GPTQ-Int4, and a browser demo; its “humanlike” blind next-message test claims a judge selected the model response as the real human message 26% of the time versus 0% for base Gemma. Top technical discussion questioned the target use case for a model optimized to mimic informal texting, with one commenter arguing much of the behavior might be approximated by a detailed system prompt specifying lowercase, short, slang-heavy Discord/iMessage style. Another commenter noted that the “asks, doesn’t guess” behavior could be valuable for voice/TTS assistants, though they also implied the model may still produce terse or awkwardly minimal replies in simple contexts. - A commenter argues the claimed “humanlike chat” behavior may be achievable with a sufficiently detailed system prompt rather than a specialized model, providing a long prompt that constrains casing, punctuation, slang, emoji usage, refusal style, and persona persistence. The prompt explicitly bans assistant-like behaviors such as “As an AI…”, “How can I help you today?”, markdown, lists, and formal grammar, suggesting the discussion is partly about prompt engineering vs. finetuning for style transfer. - One technical concern raised is evaluation of hallucination/tool-use behavior: a user reports that after only a few chats the model began inventing calendar capabilities and fictional text-message interactions, including claiming it could text someone and returning their supposed reply. They contrast this with base Gemma 4 31B, saying the base model does not exhibit the same behavior, implying the chat/persona tuning may have degraded grounding or increased roleplay-driven fabrication. - Several commenters ask for details on the training dataset and methodology, including whether the dataset can be open-sourced or at least described. This is relevant because the model’s “human voice” could come from curated conversational data, synthetic style examples, or prompt-like supervised finetuning, and the hallucination reports make dataset composition and safety filtering important for reproducibility. - Release: Swift 1.5 Flash Next - Heretic v2 - ReverseEngineer (Activity: 373): The image is a terminal-style release banner for Swift-1.5-Flash-Next-Heretic-ReverseEngineer, an uncensored Qwen/Swift 1.5 Flash Next 125B MoE GGUF quant optimized for reverse engineering workloads, with the post claiming “V2 heretic weights,” imatrix-fused 70/30, and a 96 GB VRAM fit (image). The release emphasizes a custom importance matrix over 837 entries / 399,876 tokens, adding ~43k tokens for reverse engineering and “no-refusal” domains, with bit allocation biased toward chain-of-thought, system-prompt adherence, architecture/coding, tool use, and RE tasks like x86-64/ARM64 disassembly, Ghidra critique, ELF/PE/Mach-O internals, anti-debugging, Frida/eBPF tracing, firmware analysis, and devirtualization. Quantization is described as activation-aware: late-layer down projections, layers 36–47, selected early/mid spikes, layer 0 attention gate, layer 2, and the router are preserved at higher precision/full precision based on activation-energy/KL sensitivity findings. Commenters were positive but focused on deployment tradeoffs: one requested a 128GB-oriented quant above 5 bpw with a 90–100GB backbone plus separate 8-bit n-gram/engram shard for SSD offloading, while another noted the Flash Next license likely prevents an official Swift API offering. - A user requested a 128GB-focused quantization target: >5 bpw, a roughly 90–100GB backbone, and 8-bit engrams packaged as a separate shard to make SSD offloading easier. This suggests interest in a deployment profile optimized for high-VRAM consumer/prosumer systems while keeping auxiliary memory structures separable for storage-tier placement. - The Swift team commented that they would likely build an API around Flash Next if the Flash Next license allowed it, implying licensing is currently a blocker for downstream hosted/API integrations rather than a technical limitation. - There was a practical implementation question about how to use the strata components, specifically whether they should simply be unzipped into the strata folder. This indicates the release may need clearer installation/loading instructions for its multi-component model layout. 3. Local Multimodal and Browser Inference Tooling - I made a distributed chatbot that runs a 27B model across consumer GPUs, entirely in the browser (open source) (Activity: 342): The post introduces ClusterChat, an open-source browser-only distributed LLM runtime that shards a model by transformer block ranges across multiple consumer GPUs using WebGPU for inference and WebRTC for peer coordination/data transfer. The author reports running Qwen3.8-27B q8 across a RTX 3090 + GTX 1070 Ti, and benchmarks ~8.1 tok/s across two remote 3090s with 28 ms latency versus ~20 tok/s on two local 3090s, implying network latency/bandwidth is the primary bottleneck. The image is a technical explainer/mockup showing a 27 GB model distributed across four users’ browser-connected devices contributing 3 GB, 12 GB, 8 GB, and 10 GB of GPU memory: GIF. Commenters were impressed by the feasibility of private, peer-hosted LLM clusters, with one calling it potentially “revolutionary” and another noting they had dismissed the idea after being told it was not worth pursuing. There was also speculative discussion about whether this kind of distributed redundancy resembles how a future AGI might resist centralized shutdown. - Commenters highlighted the practical significance of aggregating consumer GPUs across separate machines/locations into a private inference cluster, specifically noting that two mid-range GPU PCs could effectively approximate one larger machine for local model serving. The project provides a working implementation rather than just a concept, with a public demo at clusterchat.io/chat, private cluster creation at clusterchat.io/create, and source code on GitHub. - Kokoro can now clone any voice with just ~600 MB of extra VRAM! (Activity: 344): The post describes an any-tts recipe in audio.cpp that chains Kokoro TTS with OpenVoice’s independently runnable ToneColorConverter, effectively adding voice-conversion-based cloning to arbitrary TTS models without retraining. Reported overhead is about ~600 MB extra peak VRAM, ~10% CUDA slowdown, and roughly 0.5× original CPU speed while still achieving about 2× real-time; CAM++ speaker-similarity scores on synthetic references were reported in the 0.7–0.8 range, though quality depends strongly on the selected built-in Kokoro voice, e.g. am_michael scoring 0.4 vs am_adam reaching 0.7 on the same reference. The linked Reddit video could not be independently reviewed because Reddit returned 403 Forbidden. Top comments were skeptical of the demonstrated voice similarity, saying the samples did not sound like the implied named speakers such as Al Pacino or Morgan Freeman. The main debate is therefore not about the pipeline mechanics, but whether the showcased conversions are perceptually convincing despite the reported CAM++ scores. - Several commenters report that the advertised voice cloning quality is poor based on the provided samples: voices labeled as Pacino/Morgan allegedly do not perceptually resemble Al Pacino or Morgan Freeman. One user argues that even if an embedding or “sound signature” metric indicates similarity, the generated audio fails the more important human-listening test. - A Kokoro user says this is a weak use case for the model: while Kokoro is good at its intended TTS tasks, the showcased cloning quality is not competitive if representative of best-case output. They suggest using a purpose-built cloning-oriented system such as Pocket TTS instead of adapting Kokoro for this workflow. - We’re MOSS, the team behind MOSS-Image, MOSS-TTS, and more — Ask Us Anything! (Activity: 586): MOSS/OpenMOSS announced an AMA covering its multimodal model family across image, speech/audio, and video, including MOSS-Image, a text-to-image model with a 5B DiT backbone, Chinese/English text rendering, JSON layout control, and Apache-2.0 base/SFT weights plus inference code. The post also enumerates released speech/audio models such as MOSS-TTS, MOSS-TTSD, MOSS-TTS-Realtime, MOSS-TTS-Nano-100M, audio-understanding models like MOSS-Audio-8B-Instruct, and visual/video models including MOSS-VL, MOSS-VL-Realtime, and MOVA. The team claims MOSS-Image’s ComfyUI version has reached 3,702,490 downloads and positions the broader research agenda around synchronized multimodal perception/generation over time. Top technical requests focused almost entirely on roadmap gaps: multiple commenters asked for a MOSS-Image-Edit model, and one requested a future MOSS-TTS 2 with controllable inline delivery/prosody tags plus an audio-spectrum/tokenizer mechanism capable of capturing reference-audio intonation. - Multiple commenters asked whether MOSS plans to release an image editing model, specifically “Moss Image Edit.” One techn