Strategize Labs @StrategizeLabs

That was fast. Claude Fable 5 (Mythos) is back. Remember when the movie Big Trouble was cancelled from release back in the day because it was deemed "too sensitive"? Something similar happened to Mythos Fable 5. Fortunately, its comeback was much faster than the movie's. We are sincerely sorry if any of your active workflows were left in limbo owing to the abrupt June 13th pullback. However, after the regulatory notification review, the air is clear and Fable 5 is officially back in your #Alfrada model picker today. The model is still total overkill, though, for ordinary daily workflows, costing 24× tokens from your quota per token used (compared to 12× for GPT-5.5 or Opus 4.8). That said, when it comes to your hardest strategic bottlenecks or a massive codebase refactor, nothing else in the digital Disneyland comes close. ⚠️ The bumpy road does continue still because Fable 5 is [Not Private] (ZDR is excluded by the provider), meaning inputs and outputs are retained for safety reviews. So we recommend keeping client-confidential data, personal data, or state secrets away from this model. That’s why we’ve labeled it [Not Private] in the picker, and why our auto-router will never silently route your work here. So once those thorny grounds are cleared, throw your hardest challenges at Fable 5 and watch the magic happen. @hamzam1981 @s_tabasm @topsyyturvy #Fable5 #AgenticAI #DataCompliance #WorkflowEfficiency

Claude Sonnet 5 just dropped today and we have it live on #Alfrada on day one. This isn't your average minor model update. It is a complete structural promotion. So, if Sonnet 4.6 was the original Iron Man armor... Sonnet 5 is the nanotech suit. It packs near-Opus intelligence, a massive context window, and significantly faster reasoning depth for the same per-token pricing as the model it replaces. It’s almost like getting a massive raise without having to ask your boss. What just landed inside your picker: SOTA Coding & Agent Power: Drop your entire codebase or a massive document stack right into the chat in one go and get a long, detailed output without getting cut off (1M-Token Context / 128K Output Window). Adaptive Thinking: The model decides exactly how deeply to reason per turn. Trivial requests come back in milliseconds; complex logic triggers a digital Sherlock Holmes pass. Auto-Router Integrated: Sonnet 5 sits squarely in our Workhorse credit tier but carries a significantly higher quality score, meaning Alfrada's Auto-Router will automatically prioritize routing your complex turns to it. ⚠️ Sonnet 5 comes with a global inference profile meaning requests may route outside the EU for throughput. With EU Data Residency enabled, Sonnet 5 will be hidden from the picker, Auto-Router, and Swarms. You get a stunning mansion for the price of the beautiful villa you bought - which just means a massive context window and SOTA intelligence. Give it a go. @hamzam1981 @s_tabasm @topsyyturvy #Sonnet5 #AgenticAI #DataSecurity #WorkflowEfficiency

Your AI should present its best version of your work to you… not its first attempt. When you ask a standard model to compile a strategic competitive report or generate a margin chart, you usually get back a raw first draft. You end up playing manual quality control skimming for missing sections, catching unlabeled axes, or correcting formatting slips. You shouldn't have to be your AI's editor. So, #Alfrada just got a significant under-the-hood engine overhaul to handle the self-review for you. We are officially launching Rubric Loop Self-Correction along with Split Memory Focused Tools, and Device-Aware Local Model Picks - live inside your workspace. Here is how your workflow changes: 1. Self-Correcting AI (Rubric Loop & Performance Modes): Alfrada can now grade and revise its own drafts. Using our new performance slider, you can choose: Fast: Quick budget-model replies with no grading loops. Medium: Workhorse-tier reasoning with a single review-and-revise pass. Beast: Relentless self-correction loops (fully bounded to prevent infinite runs) paired with automated visual grading to correct chart axes and formatting errors before they ever reach you. Auto: Let Alfrada decide the effort level per turn based on the task’s complexity. (Self-review loops are billed as normal background model usage). 2. Focused Memory & Playbook Tools (Memory Split): We’ve re-engineered our memory backend, splitting the single combined tool into highly focused, validated actions (like memory_search, memory_create, and a dedicated playbook manager). This significantly increases the reliability and success rate of permanent style guides and memory saves across every selected model. 3. Device-Aware Model Picks: The model picker now probes your physical machine, maps your RAM tier, and curates only the strongest local models your device can run smoothly without thrashing your memory. So, self-grading execution. Higher-trust memory. Better local routing. Smoother experience. @hamzam1981 @s_tabasm @topsyyturvy #AI #SelfReview #AgenticAI #WorkflowEfficiency

Saudi Arabia, you've got our attention. Our latest analytics dropped and the numbers are crystal clear: 5 of our top 6 cities are in KSA. Jeddah leading at 16.8%, Riyadh at 14.4%, Dammam, Madinah, Lajis… all showing up. But here's what caught our eye. These aren't just page visits. We're seeing engagement rates hovering around 85% across these cities. People aren't clicking once and leaving. They're reading, watching, and coming back. The professional community in KSA is digging into what we're building, and the depth of that engagement tells us something real is happening here. When a region shows up this strongly and pairs it with this quality of attention, you pay attention. To everyone in Jeddah, Riyadh, Dammam, Madinah, and beyond: thank you. You're proving that the appetite for intelligent, evidence-driven tools in the region is alive and well. Keep showing up. We'll keep building. @hamzam1981 @s_tabasm @topsyyturvy #SaudiTech #DataAnalytics #KSA

Meet your new Project Manager: Z.ai's GLM 5.2 is live on #Alfrada. GLM 5.2 is what we call an "orchestration-level model" – engineered to act like a project manager. It excels at breaking down giant, multi-stage goals, planning out the exact sequence, calling our built-in tools (like code execution, document patching, and web searches) in the correct order, and keeping its "thought process" completely aligned over hours of continuous, multi-step work without losing the thread. Here is what just went live: ➡️ Massive 1M-Token Context Window: Where GLM 5.1's tighter limit forced you to trim your reference files, GLM 5.2 holds an entire codebase, deep market research libraries, or a dense portfolio data history in a single, continuous reasoning pass. ➡️ Long-Horizon Automation: Engineered explicitly to build multi-step plans, manage project-scale software refactoring, and run tool-heavy agentic swarms continuously across many turns. ➡️ Premium pricing, kept low: Sits comfortably in our SOTA tier, maintaining identical credit pricing to its predecessor ($1.40 in / $4.40 out per 1M tokens) while vastly expanding your working window. Stop babysitting your prompts. The era of agentic project management has arrived. Try now... seriously. @hamzam1981 @s_tabasm @topsyyturvy #AI #GLM52 #Agentic #Orchestration #LocalAI #WorkflowEfficiency

Well, that was fast. Just as we launched day-one access to Claude Fable 5 (Mythos), global sovereign policy caught up with us. Due to an immediate regulatory directive from the US government, Fable 5 and Mythos 5 platform access has been disabled by Anthropic. We are genuinely sorry to see it go, and apologize for any disruption this abrupt roll-back causes to your active workspaces. But when one door closes in AI, a massive open-source window opens. We have officially launched Kimi K2.7 Code, the new gold standard in developer-native code reasoning - live inside #Alfrada. Here is what just landed: ➡ Massive Performance Gains: Improved agentic capability over K2.6, boasting +10.4% on Program-Bench, +11.4% on MCP Mark Verified, and an incredible +76.2% on SWE Marathon. ➡ Tighter Reasoning Efficiency: No more overthinking. Run multi-step file manipulations with 30% lower reasoning-token usage than Kimi K2.6. ➡ Long-Horizon Success: Much stronger instruction-following and higher end-to-end task completion rates across your codebase. So a reluctant farewell to Fable 5, but a warm welcome to elite, secure open-source code reasoning with Kimi. Try it now Alfrada. @hamzam1981 @s_tabasm @topsyyturvy #KimiK27 #SWE #NoCode #OpenSource #LocalAI #WorkflowEfficie

@AnthropicAI Claude #Fable5 (aka Mythos) is available to @StrategizeLabs users on day one! Our internal #CanItRunAlfrada benchmark placed it at the very top of models that can work with our harness and thanks to our aggressive KV cache optimisation, it does not linearly double the price of #Opus4_8 despite the list price being so. On a personal level though, I think the numbers do not do it justice. Working with Mythos feels different. I can't put my finger on it but it feels a lot more insightful/thoughtful (or at least till Anthropic dumbs it down!). What I don't like is that Anthropic is refusing to make the model #ZeroDataRetention in the name of 'safety' which means we at S.Labs really cant use it for anything of significance internally. We recommend the same to our users - don't upload confidential files or information even on Alfrada. We cant stop Anthropic from training on and/or monitoring your secrets.

@bridgemindai Yep. It uses twice as many tokens as Opus 4.8. Having said that.. it is the highest quality reasoning model right now.

Absolute reasoning power. Massive 1M-token context window. Anthropic just dropped Claude Fable 5 (Mythos), and it is live in your #Alfrada model picker today. It is the highest quality reasoning model currently on Alfrada, sitting directly above Claude Opus 4.8. It is designed specifically for high-stakes, complexity-heavy, multi-constraint problems where standard SOTA-tier answers aren't airtight. What Fable 5 brings to your workspace: ➡ Unmatched Logic Resolution: Solve your hardest strategy, adversarial audits, codebase-wide refactors, and logical proofs that must hold up under board-level scrutiny. ➡ Massive 1M-Token Context Window: Ingest a full corporate data room, complex litigation files, or an entire year of research notes in a single pass - with a generous 128K output window. ➡ Adaptive Deep Reasoning: The model dynamically scales its reasoning depth. Simple tasks process instantly, while complex logic triggers a deep, highly systematic thinking pass. ⚠️ But with massive intelligence comes massive caution. Fable 5 is NOT your new default model: 🚫 ZDR Excluded [Not Private]: Anthropic does not support Zero Data Retention on Fable 5. Your prompts could be read by their safety personnel. Do not send it client-confidential data, personal data, or proprietary trade secrets. We’ve marked it clearly in the picker. 🚫 "Overkill" Pricing Band: It consumes twice as many tokens as our standard SOTA band (24× tokens vs 12× for Opus 4.8 / GPT-5.5). Reach for it deliberately. 🚫 Manual Picker Only: The Auto-Router will never silently route your queries to Fable 5, and it is completely excluded from our automated Swarm workers. Using it is always an explicit, deliberate choice. The ultimate strategic reasoning model - built for the decisions where getting it wrong is not an option. Try it now. @hamzam1981 @s_tabasm @topsyyturvy #Fable5 #Mythos #LongContext #AgenticAI #DataSecurity

30.2 billion tokens. In 7 weeks. That’s roughly 60-ish million words per active user. Since our official launch, #Alfrada has evolved. 90% of conversations actively use tools compared to less than 1% on Open Router, while 70% of our compute runs on frontier models like Claude Opus 4.8. Alfrada act. They do not just chat. Here is how we made agentic execution viable - and not just for enterprise, and why it’s scaling. → State Machines, Not Chatbots: Our agents don't wait for prompts; they run in continuous, autonomous loops for hours until a task is completely solved. → High-Efficiency Swarm Mode: Multi-headed self organising agentic Swarms now drive >50% of our usage, deploying collaborative agent armies to solve long-form business problems with maximum token efficiency. → Unforgiving Pareto Curve: On a fine-tuned state machine, a small fraction of critical loops do the heavy strategic lifting, while the rest acts as orchestration overhead. The era of high-leverage execution is here. And we're just getting warmed up. @hamzam1981 @s_tabasm @topsyyturvy #AIAgents #EnterpriseAI #CUDA #SovereignAI

You can now edit your uploaded documents in place in #Alfrada. Seemingly simple update. But major relief. We've all been there: you upload a beautifully formatted client Word document or design-agency PowerPoint deck... ask an AI to fix one thing. You either lose your formatting entirely, or open the document to make the edit yourself. Today, Alfrada just got a significant upgrade to fix that. We've launched In-Place DOCX & PPTX Editing allowing you to surgically modify uploaded files directly inside your chat workspace without losing their formatting, master layouts, or active designs. No generic reconstructions. Your formatting stays byte-for-byte identical everywhere you didn't touch. Try it now. @hamzam1981 @s_tabasm @topsyyturvy #DOCXPatch #PPTXPatch #WorkspaceAutomation

Heavy orchestration. Interactive video editing. Ever try to run a multi-step research loop only to watch your model lose its train of thought halfway through? Or generate a great video clip only to realize you have to start the entire prompt chain from scratch just to tweak a transition? Today, #Alfrada just got a double-header promotion. We've officially launched @nvidia 's flagship Nemotron 3 Ultra orchestration model alongside our new Live Video Studio v2.0 right in your Work panel. Here is what just landed: NVIDIA Nemotron 3 Ultra: A massive 550B total / 55B active MoE built specifically to manage long-context research and multi-step agentic Swarms. It planning-tracks context across dozens of tool calls – at our cost-effective Workhorse pricing. (Note: Routed globally; for strict EU residency, select Nemotron 120B [EU] on Bedrock instead). Live Video Studio v2.0: No more flat, uneditable MP4s. Get an Adobe-style horizontal editing timeline to drag-reorder, trim, set transitions, and apply color Look directly on your generated clips. Hit "Update Preview" to re-compile on our CUDA sidecar in seconds. 550B orchestration. Professional timeline editing. Zero context switching. 👉 Join us: x.com/StrategizeLabs @hamzam1981 @s_tabasm @topsyyturvy #AI #Nemotron #MoE #LiveVideoStudio #CUDA #WorkflowEfficiency

The economics of open-weights at the absolute frontier are indeed hitting a wall, but the game theory for Chinese labs like DeepSeek or Alibaba's Qwen.. is different: it's about ecosystem capture and architecture standard-setting rather than direct monetization. By subsidizing open weights, they anchor the global open-source developer stack to their frameworks. However, the real business model for open-weights isn't 'selling the model,' but using it as a high-fidelity distillation source. The commercial value is shifting from running massive, costly frontier models to using them as teachers to distill highly specialized 1B–8B edge models that can run locally at near-zero marginal cost.

HyperCard’s real superpower was its highly constrained, structured runtime (cards, backgrounds, fields, and HyperTalk). Because the state space was tightly bounded, it was almost impossible to build an app that was fundamentally "broken" at a system level. You just had logical bugs. Modern LLM-generated web apps have the opposite problem. They generate unconstrained raw HTML/JS/CSS, which creates an infinite state space. This makes initial generation magical but iterative debugging a nightmare as the code-spaghetti scales. To truly recreate the HyperCard magic, LLMs shouldn’t write raw React/HTML. They should compile natural language directly into a robust, visual state-machine runtime. We need a modern, LLM-native 'stack' interpreter that guarantees structural integrity, leaving the model to focus only on UI logic and state transitions.

Can you analyze 500K tokens of messy research without breaking your budget... and then instantly spin up collaborative slides in the same chat? Hell, yes. The all-new #MiniMaxM3 (524K context window) and our new #Canva Integration are officially live on #Alfrada. Here is what just landed: MiniMax M3: A high-capacity, budget-friendly workhorse built for long-context research and tool-heavy agentic Swarms. Select manually from model picker. Canva MCP: Connect up to three Canva accounts per workspace. Design, edit, and import slide decks or brand assets directly in-chat. 524K context. Native collaborative design. Budget-friendly pipelines. The design stack just got a promotion. 👉 Try now: alfrada.com @hamzam1981 @s_tabasm @topsyyturvy #DesignAutomation #WorkflowEfficiency

@swyx @soumithchintala @pewdiepie @opencode When DIY self-hosting reaches 10-GPU multi-agent voting setups, the bar for agent startups skyrockets. It's no longer about wrappers. It's about enterprise orchestration connecting these private nodes into cohesive, secure business systems.

@AnthropicAI @AltimeterCap @Greenoaks @sequoia $965B isn't a tech valuation; it's an infrastructure valuation. At this scale, Claude is a utility. Enterprises must stop treating LLMs as SaaS and start structuring them as foundational infrastructure on the balance sheet.