What if, instead of asking AI to build the same tool again and again, you could teach it your workflow once and reuse it? I used MiniMax Code to turn my content-production system into my new open-source Skill: build-self-media-workbench. The Skill can generate a customized bilingual creator workbench from a simple request. It connects ideas, video projects, tasks, materials and performance reviews in one working web app. The workflow is based on how I actually create content: Idea → Video Project → Tasks → Materials → Publish → Review It can also import official spreadsheet exports such as douyin and tiktok, preserve data locally, create backups and adapt the interface for mobile screens. I used MCode to structure the Skill, define the workflow rules, build the files and test the generated workbench in the browser. Then I installed the Skill and used it to create my own creator workspace. The result is not only one finished app. It is a reusable set of instructions that other creators can customize for their own names, workflows, languages and visual styles. I have now open-sourced the Skill on GitHub: Try MiniMax Code:

Most AI research demos show you a polished answer. This one showed me the disagreement that happened before the answer. I gave Ling-3.0-flash @AntLingAGI a deliberately difficult question: Do four-day workweeks actually increase productivity, or do they simply compress the same workload into fewer days? Instead of asking for a quick summary, I asked it to coordinate five specialist roles: a scientist, a data analyst, a cross-validator, an archivist, and a research writer. Each role had a separate responsibility. The scientist defined the competing hypotheses. The analyst extracted comparable findings. The archivist tracked the sources. The writer could only use approved claims. And the cross-validator had one job: challenge anything that sounded more confident than the evidence allowed. That last role changed the result. The team reviewed 12 sources and challenged six major claims. Three claims were narrowed. One was rejected entirely. Even a widely repeated claim about a 40% productivity increase did not survive the evidence check. That is the part I wanted to see from an AI research workflow. Not just more information, but visible resistance to weak evidence. The final output included: - a direct executive answer - a structured research paper - a source and evidence table - a disagreement log - a six-slide executive deck - a quality-control summary The conclusion was also more useful than a simple yes or no: reduced working hours may maintain productivity and improve wellbeing under certain conditions, while compressing the same workload into fewer days can increase fatigue and intensity. The evidence did not support a universal productivity claim. What impressed me was not that Ling-3.0-flash generated a long response. Plenty of models can do that. It was the way the model maintained multiple roles, evidence standards, objections, citations, and deliverables across one extended workflow, while preserving uncertainty instead of smoothing it away. That makes Ling-3.0-flash especially interesting for work where execution matters as much as reasoning: research, search, coding, document processing, tool use, repeated checks, and other multi-step agent workflows. The strongest AI systems will not use the largest model for every task. They will combine deep planning with fast, cost-efficient execution. Ling-3.0-flash is built for that execution layer. Ling-3.0-flash is now available on OpenRouter and free to use through August 3, 2026. Try it in your coding, search, research, and tool-use workflows. Then show us what you build. Try Ling-3.0-flash: Documentation:

Three Credible Sources, Three Different Timelines: I Asked Apodex @Apodex_AI to Judge the Foldable iPhone Rumors   As of July 16, 2026, Apple has not confirmed whether it will release a foldable iPhone before the end of the year.   The problem is not a lack of reporting. It is that credible reports point in different directions.   Bloomberg's Mark Gurman says Apple's first foldable iPhone remains on track for a September 2026 debut. Nikkei and Reuters describe engineering problems that could delay shipments. DigiTimes and MacRumors suggest production has slipped, but Apple is still targeting fall 2026.   That made the foldable iPhone a useful test for Apodex.   I asked whether Apple would both announce and begin selling its first foldable iPhone by December 31, 2026. Apodex had to identify conflicting sources, explain their weights, build three scenarios, state a confidence level, and list the signals that would invalidate its conclusion.   The distinction between "announce" and "begin selling" matters. Apple could introduce the phone in September while delaying availability until December or 2027.   How Apodex Weighed the Sources   Bloomberg's September timeline became the baseline. Gurman has a strong record on Apple product timing, and other outlets independently support a fall 2026 target. Apodex still down-weighted the claim because the timing was not final and later reporting introduced production risk.   Nikkei's engineering details were treated as credible and given substantial weight. However, Apodex did not treat a 2027 delay as the base case because the report described it as a worst-case outcome, not a confirmed schedule change.   The Barclays view received less weight because it came from a single analyst note. Still, its pattern was plausible: Apple introduced the iPhone X in September 2017 and released it in November. A September announcement followed by December sales could reconcile the reports.   What Actually Decides the Outcome   The forecast depends on hinge reliability, OLED and assembly yield, production speed, and Apple's quality threshold.   A product can be ready to announce while remaining difficult to manufacture at scale. Better yield supports fall sales; continued instability makes December or 2027 more plausible.   Three Scenarios   Apodex divided the outcome into three paths:   - Early case, about 25%: Apple announces the phone in September and begins sales in late September or October. - Base case, about 55%: Apple announces it in September, with limited retail availability beginning in December. - Delayed case, about 20%: engineering or yield problems push consumer sales into 2027.   The base case preserves Bloomberg's September introduction while accommodating the reported production delay and December-shipment forecast.   What Would Prove It Wrong?   The probability of a 2026 sale should fall sharply if Bloomberg, Reuters, Nikkei, or Apple reports that volume production has moved into 2027. The same applies if suppliers delay components into Q1 2027 or if assembly and display-yield failures continue into October.   The cleanest public test will be Apple's September event. If it passes without a foldable-iPhone announcement, the base case fails. Confirmed mass production, carrier preparation, or 2026 delivery dates would move the forecast in the opposite direction.   Why the Test Matters   The useful result was not the percentage itself. It was the structure of the judgment.   Apodex compared contradictory claims, assigned different weights, built multiple paths, and stated what would force it to change its mind. That matches its official positioning as a heavy-duty solver: turning information into evidence through verification and reaching a defensible conclusion under uncertainty.   The narrower claim is more useful: when the answer does not yet exist, Apodex can make the evidence, uncertainty, and failure conditions inspectable.   Try Apodex:

Over the past two years, AI video models have been competing on realism, resolution, and duration. But no matter how impressive the results look, we remain passive viewers: we press play, watch the clip, and it ends.   AlayaWorld @alayastd is attempting something fundamentally different. Instead of generating a fixed video, it generates a world that continues to unfold as you move through it.   These three demos show the same journey toward a green village rendered in three distinct styles: photorealistic, oil painting, and line art. As the camera moves forward, the model continues generating the road, fences, trees, and distant village. This is not simply an existing video with different filters applied. The environment is generated continuously along the camera trajectory, allowing the scene to develop as the user explores it.   AlayaWorld streams video at 720p and 24 FPS while supporting camera movement and viewpoint control. The real breakthrough is not just image quality. Once generation becomes fast enough to respond within an interactive loop, the user is no longer merely watching a video. They become a participant inside the generated world.   The world can also respond to new instructions. During generation, users can introduce prompts that trigger spells, summon characters, create explosions, or transform the environment. Most video models follow an initial prompt and produce a predetermined clip. AlayaWorld can respond to changing intent while the world is still running, allowing subsequent events to evolve according to the user’s commands.   Generating an attractive frame is relatively easy. Maintaining a coherent world over time is much harder. As a video model repeatedly predicts the next frame, small errors can accumulate until roads, buildings, and objects begin to distort or disappear. AlayaWorld combines spatial memory with compressed historical context, helping the model remember both where things are and what has already happened. This enables stable generation lasting more than one minute while improving consistency when the camera leaves an area and later returns.   This may be the next step for AI video: not simply generating a longer movie, but generating a world that can be explored, changed, and interacted with.   AlayaWorld is developed by Alaya Lab. The team is progressively releasing its inference code, training code, and datasets, with an online experience expected to launch near the end of the month.   Project page: