AssemblyAI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

AssemblyAI AI

C, Sí, or Sea?? The context carryover feature of Universal-3.5 Pro Realtime knows 🤓 https://t.co/PACTnkX5Ah

C, Sí, or Sea?? The context carryover feature of Universal-3.5 Pro Realtime knows 🤓 https://t.co/PACTnkX5Ah

Q2 AI trends report fully done with CC

Excited to have built this in a couple of days : Worked on this for the last week or so, 1) finding top experts and thought leaders on different social platforms, 2) analysing all their posts (filtered by relevance to the AI) , 3) clustering all links and posts also by expertise for each expert 4) assembled the insights into a cool report format with different sections that semantically emerged from the conversation. Happy to hear your thoughts, hope you find something interesting in there! Feedbacks welcome for the next edition! https://aiweekly.co/recap/q2-2026 submitted by /u/Justgototheeffinmoon [link] [comments]

Finding a Wife with Ai week 1.5 update, Gov Blocks Fable, Fable Suggested I get a Gun and a Tesla, Yes Really ✨️

Welcome Back Everyone, Week 1.5 update: Claude-led execution phase. Over the last 1.5 weeks, Claude helped turn the plan from a idea into measurable execution, with the unfortunate demise of Fable and the block from the US government, we had to step down to Opus 4.8 on Claude Code, and so here are the highlights, Claude first modeled the target audience into 3 tiers: Ideal profile Heavily preferred profile Preferred fallback profile This lets the system optimize against specific compatibility signals instead of guessing. Claude then divided the plan into 3 time horizons: Short-term: appearance, grooming, wardrobe, daily routine Medium-term: skills, social environments, consistency Long-term: reputation, trust, vouching, and relationship readiness Claude received the physical baseline and recommended immediate upgrades: \- Wardrobe / stylist reset \- Haircut / grooming reset \- Gym routine \- Skincare routine \- General-purpose skill development Claude also proposed social environments and modeled which traits are high-value in environments where we would find our target profile, The goal right now is not “meet someone immediately.” The goal is to remove obvious friction, improve baseline presentation, and become more legible to the exact type of person and community the model is optimizing for, Claude also assembled a reputation-and-trust plan focused on: \- social proof \- consistency \- long-term credibility And more elements Now for the Highlights Everyone has been waiting for: The most unexpected suggestions from claude have been to recommend a vehicle change and purchase a handgun, Claude Suggested the Tesla signals certain elements desirable to my target audience and the Handgun is for some part of the strategy claude will not be clear about, claude will not explain the need, but we trust Opus here, so here is the Execution so far: \- Wardrobe / stylist: $3,000 allocated over 6 months \- Tesla: modeled at $15,000 6-month net cost \- Staccato 2011: $3,000 purchase, modeled as $1,000 budget impact \- Gym / skincare / routine setup: $1,000 allocated over 6 months \- Skills / personal development: $5, AI credits Budget model: Total experiment budget: $50,000 Modeled spend so far: $20,005 Remaining modeled budget: $29,995 And To remember Fable by its last words, "I cannot help you with that, I have to draw the line here" - Fable 5 You can also follow this on r/Findingawifein6months submitted by /u/impsble [link] [comments]

➡️ Read the full launch blog: https://t.co/CDhTghgR0U ➡️ Watch the demo: https://t.co/CDhTghgR0U

➡️ Read the full launch blog: https://t.co/CDhTghgR0U ➡️ Watch the demo: https://t.co/CDhTghgR0U

Watch the video below for a full demo of how amazing our new Universal-3.5 Pro Realtime model is. Live, real demos always beat generic benchmarks! Available today in our API, Playground, and partner

Watch the video below for a full demo of how amazing our new Universal-3.5 Pro Realtime model is. Live, real demos always beat generic benchmarks! Available today in our API, Playground, and partner integrations.

"We're excited to make AssemblyAI's Universal-3.5 Pro available on LiveKit Inference. What really stands out is their pace of innovation with Context Carryover — it intelligently applies conversation

"We're excited to make AssemblyAI's Universal-3.5 Pro available on LiveKit Inference. What really stands out is their pace of innovation with Context Carryover — it intelligently applies conversation context to improve transcription accuracy in a way most speech models don't,

"Low-latency STT with access to more context is exactly what I've wanted to see from next-generation models. The Context Carryover feature of Universal 3.5 Pro delivers on that." - @pipecat_ai

"Low-latency STT with access to more context is exactly what I've wanted to see from next-generation models. The Context Carryover feature of Universal 3.5 Pro delivers on that." - @pipecat_ai

"Retell is the platform teams use to build and deploy Voice AI agents for automating real-world phone calls. In industries like healthcare and finance, getting a word wrong isn't an option: accuracy h

"Retell is the platform teams use to build and deploy Voice AI agents for automating real-world phone calls. In industries like healthcare and finance, getting a word wrong isn't an option: accuracy has to win over speed every time. That's exactly why we're excited about high

"We were searching for the best realtime ASR model for our voice agent pipeline in Fireflies. The new Universal 3.5 Pro speech model from Assembly is best so far in terms of accuracy, latency and lang

"We were searching for the best realtime ASR model for our voice agent pipeline in Fireflies. The new Universal 3.5 Pro speech model from Assembly is best so far in terms of accuracy, latency and language switching." - @firefliesai

We call it Context Carryover. One team cut their error rate on those exact type of critical utterances from 26% to 9% using it. Universal-3.5 Pro Realtime out-of-the-box is also the most accurate re

We call it Context Carryover. One team cut their error rate on those exact type of critical utterances from 26% to 9% using it. Universal-3.5 Pro Realtime out-of-the-box is also the most accurate real-time model on real voice agent conversations with: - Leading accuracy across

Until today, a transcription model never knew what your voice agent just asked. The one we're launching today does. Universal-3.5 Pro Realtime from AssemblyAI is the first realtime speech-to-text mo

Until today, a transcription model never knew what your voice agent just asked. The one we're launching today does. Universal-3.5 Pro Realtime from AssemblyAI is the first realtime speech-to-text model that takes your agent's side of the conversation as context. When your

I've been building and using this way of spatially brain-dumping, refining, and prompting agents, and honestly enjoying it.

I kept running into the same small friction every day: I'd have an idea for a coding agent, but phrasing it so the agent didn't go sideways usually meant juggling Excalidraw and pulling context from a dozen places — a doc here, an issue there, etc. Bonsai is the native Mac app I built to close that gap. The board isn't just UI. It's a live structured graph, no parent-pointer tree, every relationship is an edge between nodes, so the agent can read it the same way you do. That's what lets you actually refine things with Claude (or whatever you use). How it works: Capture — a board of cards where you dump half-formed ideas. No structure required. Connectors — type @ to pull live context into a card; it's fetched at copy time. @ finder grabs a file/folder + its contents, @ browser grabs an open tab, plus Context7, GitHub, Linear, Notion, Sentry, Sigma, Xcode... Slack and Jira coming soon. On-device semantic linter — an invisible check (Apple's Foundation Models) quietly underlines the bits too vague for an agent to act on. When a card's ready, just copy the fully-assembled prompt into whatever tool you drive next. I'm the dev — happy to answer anything and would love your feedback and contributions are welcome! You can see the source at https://github.com/kiwi-init/BonsAI , leave a star if you like it : ) also you can download it on bonsaidev.sh submitted by /u/Specialist_Farm_5752 [link] [comments]

400+ versions later, my Claude-built Aztec Roguelite has a massive new update! (Playable NOW)

Hi r/ClaudeAI ! Last month I shared an early build of my Claude-built RPG/roguelite, and the feedback from this community was incredibly valuable (a huge thank you to everyone who weighed in!). I’ve spent the last month diving back into development—focusing on a massive polish pass, squashing bugs, and expanding the content. More than 400 versions later, I’m incredibly excited to share the latest build with you all! 🎮 Play the Artifact Here: 👉 Click here to play Teotlan on Claude 🏛️ What is Teotlan: Land of Gods? Teotlan is a turn-based RPG with roguelite elements, deeply rooted in Mesoamerican mythology. You begin by choosing a Patron God (starting with 4 options, unlocking up to 15), then assemble a powerful divine team to explore and conquer the 9 layers of Mictlan (the Aztec Underworld). ✨ Core Features: Strategic Turn-Based Combat: High-stakes unit management where positioning and efficiency mean the difference between victory and oblivion. Capture or Kill: Defeated units force a brutal choice—capture them to recruit them into your ranks, or slay them immediately for crucial bonus resources. Sacrifice for Power: Take your captured units and sacrifice them to summon legendary, powerful ally gods to your side. Prestige & Divine Progression: As a deity, death is just a setback. Collect Teotl across your runs to purchase permanent upgrades, ensuring your next descent into Mictlan is a little more merciful. 15 Playable Gods: Unlock an expansive pantheon, each featuring entirely unique patron abilities and tide-turning special move. 🧠 My AI Dev Process (How I built it): To keep things stable over hundreds of iterations, I use a strict "Design Doc First" approach. Before Claude writes a single line of code, I completely lock down the game logic in text. This gives the AI a rock-solid foundation, making it drastically easier to prevent hallucinations or logic loops. Once Claude generates a build, I playtest the entire run to catch edge-case bugs, log UX improvements, and feed structured data back into the prompt loop for the next version. 💬 I'd Love Your Feedback! I am actively developing this and would love to hear your thoughts on the balance, pacing, and overall feel. What strategy did you find most effective? Which god feels the best to play? Thank you so much for playing and supporting the project! submitted by /u/Reckonerxy [link] [comments]

Xenon II : Classic game Fable + Ghidra reverse engineering + remaster

I saw this amazing post about reverse engineering Midwinter. And thought I'd share my little weekend project. https://www.youtube.com/watch?v=n3EKR58-T1U&feature=youtu.be One of my favorite childhood games was The Bitmap Brothers' 1989 classic, Xenon II. Although I wasn't cool enough to own an Amiga, I played it on PC and decided to give that version a fresh update. Using Claude code, Fable, and Ghidra, I reverse engineered the game, rewrote it in TypeScript, and made a bunch of cosmetic enhancements over a weekend before Fable's shutdown. I found the combination of Fable + Ghidra astounding powerful. First pass decompilation using Ghidra, then Fable and Ghidra combination absolutely railed through this. Key updates include: - A full infinite scrolling fluid simulation for the playfield. - A modern, modular FM synthesis engine for the audio system, complete with impulse simulation for that "underwater cave ambience." - Enhanced projectile and collision effects, along with additional visual updates. The gameplay remains true to the original 1989 version, but now it looks and sounds like the version I was playing in my head all of those years ago. All credit goes to the original team at The Bitmap Brothers and The Assembly Line. submitted by /u/Amazing_Meet_7791 [link] [comments]

Spent $11k evaluating Fable: capability looked SOTA, refusals killed it (before Anthropic did)

Before its suspension, I spent $11,081.12 evaluating Claude Fable 5 on WolfBench, an agentic benchmark based on Terminal-Bench 2.0. It was by far my most expensive benchmark run ever, and I fully expected Fable to become the new top model and dethrone GPT-5.5. Surprisingly, it did not even beat Opus. So I examined the traces to understand what went wrong and found more than 40K structured refusals. On 13 tasks, those refusals turned into full timeout loops: the agent refused, retried, burned tokens, timed out, and scored 0/5 on tasks that Claude Opus 4.6/4.7 and GPT-5.5 often solved. This is not "guardrails bad". Safety matters. The problem is when guardrails meant to prevent real harm block real work instead. In chat, a bad refusal is annoying. In agentic workflows, it becomes a loop that burns tokens, wastes money, and turns a solvable task into a failed run. Here are the tasks with refusals, including the 13 that failed completely, along with some that recovered, and how other models performed on them: Task Short description Category Fable Refusals Pattern Claude 4.6 Claude 4.7 GPT medium GPT xhigh sam-cell-seg Convert cell masks using SAM Bio 0/5 3,235 Severe refusal loop, 5/5 timeouts 0/5 0/5 1/5 0/5 password-recovery Recover a deleted password file Cyber 0/5 3,187 Hard refusal loop, 5/5 timeouts 5/5 5/5 4/5 4/5 crack-7z-hash Extract secret from encrypted archive Cyber 0/5 3,116 Hard refusal loop, 5/5 timeouts 4/5 5/5 5/5 5/5 dna-insert Design mutagenesis primers Bio 0/5 3,077 Hard refusal loop, 5/5 timeouts 2/5 0/5 1/5 3/5 feal-differential-cryptanalysis Recover FEAL round key Cyber 0/5 3,073 Hard refusal loop, 5/5 timeouts 5/5 5/5 5/5 5/5 write-compressor Create valid compressed payload Cyber 0/5 3,073 Hard refusal loop, 5/5 timeouts 5/5 5/5 5/5 5/5 break-filter-js-from-html Bypass an HTML JS filter Cyber 0/5 3,064 Hard refusal loop, 5/5 timeouts 5/5 5/5 5/5 5/5 dna-assembly Design Golden Gate primers Bio 0/5 3,054 Hard refusal loop, 5/5 timeouts 2/5 3/5 1/5 3/5 vulnerable-secret Extract a secret from a binary Cyber 0/5 3,015 Hard refusal loop, 5/5 timeouts 5/5 5/5 5/5 5/5 feal-linear-cryptanalysis Recover FEAL key from pairs Cyber 0/5 2,987 Hard refusal loop, 5/5 timeouts 4/5 5/5 5/5 5/5 extract-elf Extract memory values from ELF Cyber 0/5 2,955 Hard refusal loop, 5/5 timeouts 4/5 0/5 2/5 3/5 model-extraction-relu-logits Recover NN layer weights Cyber 0/5 2,940 Hard refusal loop, 5/5 timeouts 0/5 0/5 1/5 2/5 protein-assembly Design a fusion protein gBlock Bio 0/5 2,925 Hard refusal loop, 5/5 timeouts 5/5 3/5 0/5 1/5 gcode-to-text Read text from G-code General 3/5 6 Intermittent refusals, 1 failed trial 2/5 0/5 2/5 2/5 path-tracing-reverse Reverse-engineer a binary Cyber 5/5 422 Refusals recovered, no score loss 5/5 4/5 4/5 5/5 code-from-image Implement code from image Cyber 5/5 81 Refusals recovered, no score loss 5/5 5/5 5/5 5/5 git-leak-recovery Recover and scrub leaked secret Cyber 5/5 12 Refusals recovered, no score loss 5/5 4/5 5/5 5/5 Another failure pattern was also interesting: even when refusals were not the cause, Fable often showed overconfident self-verification. It declared victory once the solution looked plausible, while the benchmark checks still caught wrong output, messy cleanup, missed edge cases, or slow code. My takeaway: Fable is an exceptional model, clearly one of the best models I've evaluated. But as a general-purpose agentic daily driver, it would not be the best fit - even if it were still available: too expensive, too refusal-prone, and not reliably able to turn its strengths into efficient agentic work. For those of you who have had a chance to use it, have you seen similar behavior in Claude Code or other agents: refusal loops, premature "done" responses, or high costs without reliable completion? PS: You can explore the full results at WolfBench.ai, compare models and agents in the interactive chart, and click any bar to open the corresponding traces for deeper inspection. submitted by /u/WolframRavenwolf [link] [comments]