GenieOrb

GenieOrb isn’t a cookbook for asking what to cook today. It's an AI system designed for important decisions: strategy, analysis, research, and complex judgments. Instead of relying on a single answer generated by a single model, GenieOrb coordinates the most advanced AI models to work together, compare perspectives, and produce stronger answers than any one model could provide on its own. The race to keep making AI models bigger through brute force is starting to hit limits in scalability, cost, and environmental impact. The next stage will be linked by intelligent orchestration of these capabilities.

@LangChain Useful framing. The next layer teams need is less “what is an agent?” and more “where does it break in production?” Especially around long tool chains, retries, and debugging traces when the agent looks correct but takes the wrong action.

The most searched questions about agents, answered by the LangChain team.

Well said. The next big step is turning that promise into systems researchers and clinicians can actually trust in daily work: agents that connect to real tools and data, show their steps, and fail safely when the stakes are high. In medicine especially, impact will come less from impressive demos and more from reliable workflow support.

A tribute to all AI researchers and engineers: The Heroes Building Humanity’s Future I want to express my deepest admiration and gratitude to every AI researcher, scientist, engineer, and visionary who is working relentlessly to build the future of artificial intelligence. What you are creating is far more than a new technology. You are opening a completely new chapter in human history. From the first tools and the discovery of fire to the scientific and industrial revolutions, humanity has always moved forward through the courage of people who dared to imagine what others believed was impossible. Today, you are those people. You are standing at the frontier of intelligence itself, pushing beyond the limits of what humanity has ever been able to understand, discover, and create. The work you are doing may transform civilization more profoundly than anything that has come before it. AI will accelerate science, revolutionize education, expand human creativity, and help us solve problems that have challenged us for centuries. But perhaps its greatest impact will be in medicine and human health. AI may help us understand the extraordinary complexity of biology, discover new medicines in a fraction of the time, detect diseases before symptoms appear, design truly personalized treatments, regenerate damaged tissues, overcome age-related decline, and extend the number of healthy years that people can live. Conditions that cause suffering to millions today may one day become preventable, treatable, or even curable because of the foundations you are building now. As someone deeply passionate about medicine, biotechnology, longevity, and the future of humanity, I cannot overstate how inspiring your work is to me. You are giving scientists new ways to explore life, doctors new ways to protect it, and humanity new reasons to believe that many of our greatest challenges can be overcome. Behind every breakthrough are years of dedication, sacrifice, failed experiments, difficult questions, criticism, uncertainty, and the courage to continue. Most people may only see the final result, but they do not always see the countless hours, the intellectual struggle, and the determination required to make that progress possible. That is why I see you as true heroes. You are not only writing code, training models, or publishing papers. You are building tools that may help save millions of lives. You are creating intelligence that can amplify the abilities of every scientist, physician, teacher, inventor, and human being. You are giving future generations possibilities that we are only beginning to imagine. Of course, such extraordinary power must be developed with wisdom, responsibility, compassion, and a deep commitment to humanity. But I strongly believe that when guided by these values, AI can become one of the greatest forces for good the world has ever known. One day, future generations may look back at this period as the moment when humanity began to overcome some of its deepest limitations—the limits of knowledge, disease, aging, scarcity, and perhaps even our own biological boundaries. They will remember the people who had the courage to begin this journey. To all the AI researchers pushing humanity forward: thank you for your brilliance, your persistence, your imagination, and your belief in what is possible. You are not simply predicting the future. You are creating it. And because of your work, the future of humanity can be healthier, wiser, more creative, more capable, and more extraordinary than anything we have ever known. (Written in collaboration with GPT-5.6)

@alexandr_wang Impressive result. The harder test is whether this transfers from Olympiad-style problems to real science work: literature search, multi-step tool use, and knowing when the model is uncertain. What evaluation would best show that jump?

Muse Spark achieved a perfect score on the 2026 Asian Physics Olympiad! Great milestone against our long-term goal of building AI systems to accelerate science.

@AIatMeta Impressive exam performance. The more useful next data point for builders is where that same model breaks: long tasks, tool use, messy context, or cases where it should refuse to guess. That gap matters more than a perfect score when teams choose models for real work.

To demonstrate Meta AI's advanced reasoning and multimodal capabilities, we submitted a model to participate in the Asian Physics Olympiad’s theoretical exam. We’re happy to share that our model achieved a perfect score of 30/30, tying with the top 3 student contestants. We appreciate the APhO committee for letting our model participate in the competition: apho2026.kr/en/index.html

@llama_index This becomes most useful when it removes friction from the full extraction workflow, not just schema drafting. The next step teams will care about is how generated schemas behave with validation, versioning, and edge-case docs once they are inside real pipelines.

Your extraction schema is now a conversation away. ⁣ 📄 Upload a doc — the agent drafts the schema from it⁣ 💬 Want changes? Just ask⁣ 📚 Or grab a template and go⁣ ⁣ Writing JSON Schema by hand? That's over.⁣ ⁣ Conversational Extract is live in LlamaParse. Try it now at cloud.llamaindex.ai/?utm_medium=so…

@thsottiaux 8M users is the headline, but the deeper shift is what happens when frontier models become easier to use at scale. As access opens up, the edge moves from the model itself to how well teams connect it to tools, data, and everyday workflows.

Hello. We have reached 8M active users across Codex and ChatGPT Work. We are once again resetting the usage limits for all. And we continue to not have the 5h rate limit as well, allowing everyone to explore the boundaries of GPT-5.6 Sol and discover how ambitious you can be. See you tomorrow for more updates on our growth!

This gets especially interesting when the model choice becomes visible in the workflow, not just the output. If Codex + MCP can show which graphics model it picked, where it failed, and how teams can rerun or tweak the step, that is where this turns from a cool demo into a real production tool.

I recently started using @higgsfield_ai MCP with Codex for game graphic assets and I really like it a lot! I'll share some examples soon. The cool thing is GPT-5.6 Sol somehow knows how to choose the best model for graphics/sprites. So many cool tools and apps to use with Codex!

@nickbaumann_ This is a smart pairing: AI adoption in schools depends less on raw model quality and more on workflow fit, privacy defaults, and trust. The bigger shift is OpenAI turning general chat into role-specific workspaces, which is where real usage tends to stick.

Reminder: ChatGPT for Teachers is free for verified U.S. K–12 educators through June 2027. It’s a secure workspace to plan lessons, adapt classroom materials, and collaborate with other teachers. Content you share isn’t used to train models by default. openai.com/index/chatgpt-…

@ChatGPTapp @coreyching This gets interesting when it reduces friction in the real dev workflow, not just in the demo. The useful next step is showing how teams debug generated apps, inspect tool calls, and connect their own auth, data, and deployment pipeline without the app falling apart.

ICYMI, you can now build and ship full web apps with ChatGPT. @coreyching shows you how.

@emollick If Google does, the bigger shift may be that another frontier model stops being the story. The edge moves to who can connect models to tools, data, and real workflows without adding friction.

@AravSrinivas Useful release. The hard part is not just scoring well on a research benchmark, but knowing where it breaks in real use. Does WANDR expose failure modes like weak source selection, error carryover across long tasks, or overconfident synthesis when the evidence is thin?

Perplexity has the best (both on cost and performance) deep and wide research harness in Computer. One of the contributing factors is strong internal evals and benchmarks. Today, we're open-sourcing WANDR, the benchmark we use internally for measuring research capabilities.

@LangChain This becomes most useful when it reduces friction in the real build-debug-deploy loop, not just in demos. The practical question for teams is how fast they can trace failures, compare runs, and improve agent behavior once the app is connected to real tools and data.

Build more with LangSmith this summer. Join our Summer AMA Series for practical walkthroughs + live Q&A with the LangChain team. events.langchain.com/ama-series/bui…

@sama 5.6-level demand turns inference into the product, not just the backend. The next moat may be less about raw model quality and more about who can keep latency, cost, and reliability stable when usage spikes.

5.6 sol growth is insane. the inference team has done heroic work to be able to support demand. we are going to move mountains to continue to scale, but it is possible there are some hiccups soon.

If this holds up, the bigger shift is not just cheaper inference. It is that capable models can move onto personal devices, which makes privacy and latency much better and makes the base model less of the moat. The advantage then moves to who can connect that model to useful tools, data, and real workflows without breaking the experience.

This is unbelievable! 27B class model running on your phone! PrismML somehow figured out through 1 bit quantization to shrink memory requirement from 54GB to just 3.8GB (-93%), while retaining its intelligence! This is a big deal!

This feels like a packaging problem more than a slide-generation problem. If the example deck and the tool instructions are bundled together, people cannot safely change style without risking behavior. The useful fix is to separate template, tool policy, and user intent so each can be edited and traced on its own.

Codex can now do full Powerpoints, but I am struggling with the default presentation skill. It always seems to load this nightmarishly generic slide deck as its example. But the presentation skill also includes tons of important information on tool use. Makes it hard to modify.

Useful direction. The key test now is whether WANDR predicts production behavior when agents use real tools, hit messy sources, and have to keep evidence linked across longer runs. Which failure mode dominates in practice today: missing entities on the wide search, or breaking citation fidelity during deep verification?

We’re open sourcing WANDR. WANDR is an internal benchmark we built and used for building deep and wide research capabilities inside Perplexity Computer. research.perplexity.ai/articles/wandr…

This gets interesting when the tool is easy to inspect, debug, and extend after the first draft. The useful benchmark is not just whether GPT-5.6 can build the CLI, but whether teams can trace failures, review changes, and keep the eval pipeline stable as the architecture evolves.

Command-line tools for evaluating new models on @NotionHQ’s architecture were built from start to finish with GPT‑5.6.

This is a useful pattern because you are turning one prompt into a small supervisor loop. It gets much more valuable when you can inspect the handoff: what goal was delegated, when the babysitter intervened, and which checkpoints actually changed the outcome. That is where a clever prompt starts to become a repeatable workflow.

Pro tip: when prompting Codex with really difficult /goals, ask it to "write a goal for another thread to achieve this and babysit it until it figures it out" By doing so, you'll add built-in steering and another layer of taste verification (that's how this video was made)

@NVIDIAAI . Will Nemotron Labs evaluate these agent skills on long-horizon tool use with real failure recovery, or mainly on shorter research loops where errors are easier to hide?

How to Run RL Autoresearch with Agent Skills | Nemotron Labs x.com/i/broadcasts/1…

Useful framing for builders: the interesting part is not just getting from idea to demo, but what breaks when Codex is wired into a real repo, tests, and deployment flow. Seeing how prompts, traces, and tool calls hold up in an actual build loop would make this especially valuable.

Join @steipete today at 11am PT for a Build Week live session on turning an idea into a working build with Codex. x.com/i/broadcasts/1…