๐๐ก๐ฉ๐-๐๐-๐๐๐ฅ๐๐๐ ๐ช๐๐๐๐๐ฌ ๐๐ ๐๐๐๐ง๐๐ข๐ก - ๐ฎ๐ณ ๐ก๐๐๐ฅ๐ง ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐ฒ๐ ๐ฎ๐ป ๐๐ ๐ฎ๐ป๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป๐ฎ๐น-๐ง๐ต๐ถ๐ป๐ธ๐ถ๐ป๐ด ๐ ๐ผ๐ฑ๐๐น๐ฒ ๐ณ๐ผ๐ฟ ๐๐น๐ฎ๐๐๐ฒ๐ ๐ต–๐ญ๐ฎ
๐๐ก๐ฉ๐-๐๐-๐๐๐ฅ๐๐๐ ๐ช๐๐๐๐๐ฌ ๐๐ ๐๐๐๐ง๐๐ข๐ก
๐ค ๐ช๐๐๐๐๐ฌ ๐๐ ๐ก๐๐ช๐ฆ
๐ฐ ๐๐ ๐ก๐๐ช๐ฆ
๐ญ. ๐ธ ๐๐น๐ฎ๐ฝ๐ฝ๐ถ๐ป๐ด ๐๐ถ๐ฟ๐ฝ๐น๐ฎ๐ป๐ฒ๐ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ $๐ญ ๐๐ถ๐น๐น๐ถ๐ผ๐ป ๐ฎ๐ ๐ฎ $๐ฒ ๐๐ถ๐น๐น๐ถ๐ผ๐ป ๐ฉ๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป 
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
AI research startup Flapping Airplanes reportedly raised $1 billion at a $6 billion valuation, making it one of the latest heavily funded labs built around frontier research before broad commercial products. The financing was led by Index Ventures and Lux Capital, with participation from Sequoia Capital, GV and Kleiner Perkins. Founded in late 2025, the company belongs to a growing group of research-first labs seeking to develop advanced AI capabilities before establishing a large commercial business. The round signals investor willingness to make substantial bets on new research teams, although the company has not yet introduced a widely available flagship product.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
Flapping Airplanes is positioning itself as a research-first AI laboratory. Its funding is intended to support advanced research, although specific product plans, public performance results and practical applications remain limited.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐ฌ AI research: Explore new model architectures and learning methods.
๐ง Advanced reasoning: Investigate capabilities beyond conventional chatbot tasks.
๐ข Enterprise innovation: Potentially develop future AI systems for specialized work.
๐ป AI infrastructure: Support the computing and research teams needed for model development.
๐ฎ. ☁️ ๐๐ฎ๐๐๐ผ๐ป๐ฎ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ $๐ฒ๐ฌ ๐ ๐ถ๐น๐น๐ถ๐ผ๐ป ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐๐๐ ๐๐-๐๐ด๐ฒ๐ป๐ ๐๐น๐ผ๐๐ฑ 
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
Daytona announced a $60 million Series B round at a $1 billion valuation to expand what it calls an agent cloud: infrastructure that gives AI agents dedicated, isolated computing environments. Led by FirstMark Growth, the financing brings Daytona's total raised capital to approximately $91 million. The company argues that agents need full computer environments—not merely model access—to execute coding and other multistep tasks. Its sandboxes are designed to reduce the risk of agents damaging systems or exposing data, while giving developers a repeatable place to run tools, code and workflows. The round reflects growing demand for infrastructure built specifically for autonomous software.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
Daytona provides isolated computing environments where AI agents can execute code, run tools and work through longer tasks. The infrastructure is designed to make agent execution easier to reproduce while limiting access to sensitive systems.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐ป Coding agents: Create and test software in isolated environments.
๐งช Automated testing: Run repeatable builds and development checks.
๐ Security: Reduce the risks of uncontrolled agent execution.
⚙️ Enterprise automation: Support agents that need dedicated computing environments.
๐ฏ. ๐งฌ ๐ก๐ฉ๐๐๐๐ ๐ฎ๐ป๐ฑ ๐จ๐ช ๐ ๐ฒ๐ฑ๐ถ๐ฐ๐ถ๐ป๐ฒ ๐ฅ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ ๐ฅ๐๐๐ฐ-๐ฃ๐ฟ๐ผ๐๐ฒ๐ถ๐ป๐ฎ ๐ณ๐ผ๐ฟ ๐ฃ๐ฟ๐ผ๐๐ฒ๐ถ๐ป ๐๐ฒ๐๐ถ๐ด๐ป
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
NVIDIA and the University of Washington's Institute for Protein Design introduced RFD4-Proteina, an open generative model for designing proteins with specific functions. It co-designs amino-acid sequences and all-atom structures, using reward-guided search during inference to focus computation on promising candidates. Making the model available to researchers allows teams to investigate new protein designs and build on its capabilities. The technology could help scientists explore a huge space of possible molecular structures more efficiently than exhaustive search. However, computer-generated candidates are not automatically successful drugs: proposed proteins still require laboratory testing to establish whether they fold correctly, perform their intended functions and meet safety requirements.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
RFD4-Proteina generates protein sequences and corresponding structures while searching for candidates that meet specified design goals. Its open availability allows researchers to explore, modify and evaluate protein-design methods for their own experiments.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐ Drug discovery: Explore proteins that could become therapeutic candidates.
๐งช Biotechnology: Design molecules with specific biological functions.
๐ฌ Laboratory research: Generate candidates for experimental validation.
๐งฌ Molecular engineering: Investigate new protein structures beyond those found naturally.
๐ฐ. ๐ข ๐จ๐ฏ๐ฒ๐ฟ ๐ฅ๐ผ๐น๐น๐ ๐ข๐๐ ๐๐ต๐ฎ๐๐๐ฃ๐ง ๐ช๐ผ๐ฟ๐ธ ๐๐ผ ๐ ๐ผ๐ฟ๐ฒ ๐ง๐ต๐ฎ๐ป ๐ฏ๐ฌ,๐ฌ๐ฌ๐ฌ ๐๐บ๐ฝ๐น๐ผ๐๐ฒ๐ฒ๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
Uber announced the company-wide rollout of ChatGPT Work to more than 30,000 employees globally, expanding from an initial pilot of around 1,000 workers. The enterprise AI agent is designed to handle multistep knowledge-work tasks by connecting to applications such as Google Drive, Gmail, Google Calendar and Slack. Uber says employees can use it to research topics, prepare meeting briefs, create presentations, analyze operational data and complete workflows across applications. The deployment is a significant example of a large company extending an agentic AI platform across its workforce. However, the announcement does not independently establish productivity gains across every department.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
ChatGPT Work connects information across supported business applications and helps employees complete multistep tasks. It can assist with research, document preparation, data analysis and workflow coordination while employees remain responsible for reviewing the results.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐ Operations: Analyze business data and prepare recommendations.
๐ Meetings: Prepare briefs and organize information from calendars.
๐ Presentations: Turn research and documents into structured materials.
๐ Cross-team work: Coordinate tasks across connected business applications.
๐ฑ. ๐ง๐ป ๐ ๐ผ๐ฑ๐๐น๐ฎ๐ฟ ๐ ๐ฎ๐ธ๐ฒ๐ ๐๐๐ ๐๐๐น๐น ๐ ๐๐ซ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด ๐ฆ๐๐ฎ๐ฐ๐ธ ๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ-๐๐๐ฎ๐ถ๐น๐ฎ๐ฏ๐น๐ฒ
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
Modular made the full source code for its MAX computing stack publicly available and introduced a simplified MAX License permitting commercial products without licensing fees or royalties. The release opens components that had remained closed, including the graph compiler, runtime and hardware backends supporting CPUs and GPUs. It follows the company's earlier move to open its Mojo compiler and toolchain, completing more of its software stack for developers and hardware makers. The change could make MAX more accessible for customized AI deployment and experimentation. However, the release is accurately described as source-available under Modular's license rather than automatically equivalent to every definition of open source.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
MAX provides software components for compiling and running AI workloads across supported hardware. With the source available, developers can inspect, modify and extend more of the stack, including its compiler, runtime and hardware integrations.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐ ️ AI development: Customize components for specific workloads.
๐ป Model deployment: Build and adapt AI applications using the MAX stack.
⚙️ Hardware integration: Experiment with CPU and GPU support.
๐ค Developer collaboration: Inspect implementation details and contribute improvements.
๐ฒ. ⚡ ๐๐น๐ผ๐๐ฑ๐ณ๐น๐ฎ๐ฟ๐ฒ ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐ฒ๐ ๐๐น๐ฎ๐ถ๐ฟ๐ถ๐ฐ, ๐ฎ๐ป ๐๐ ๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป ๐ ๐ผ๐ฑ๐ฒ๐น ๐ง๐ต๐ฎ๐ ๐ฅ๐ฒ๐๐๐ฟ๐ป๐ ๐ฆ๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ๐ฑ ๐ฉ๐ฎ๐น๐๐ฒ๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
Cloudflare introduced Clairic in preview on Workers AI, describing it as an extraction model that returns structured values instead of generating conventional text responses. Developers provide unstructured text, optional images and a schema specifying fields such as dates, numbers, lists or estimates. Clairic returns values in the requested types—or null when the input does not support an answer. It also provides confidence-related signals and character offsets for extracted text. Cloudflare reported that a pipeline using Clairic completed one customer-support claim task faster and at lower cost than an LLM-heavy comparison. Those figures are company-reported results from a particular test, not a universal performance guarantee.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
Clairic extracts, computes, estimates and forecasts typed values from text and images. By returning structured outputs rather than lengthy responses, it can support applications that need specific facts, confidence signals and traceable text locations.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐งพ Invoice processing: Extract totals, dates and itemized values.
๐ฆ Customer support: Identify facts from damaged-product or delivery claims.
๐ฅ Document processing: Convert unstructured records into structured fields.
๐ Data workflows: Prepare information for databases, analytics and automated decisions.
๐ณ. ๐ ๐ฃ๐ถ๐ด๐บ๐ฒ๐ป๐ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ $๐ฎ๐ฑ๐ฌ ๐ ๐ถ๐น๐น๐ถ๐ผ๐ป ๐ฎ๐ ๐ฎ $๐ฎ.๐ฏ ๐๐ถ๐น๐น๐ถ๐ผ๐ป ๐ฉ๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
Business-planning platform Pigment raised $250 million in a Series E round at a reported valuation of $2.3 billion. The round was led by Scaleup Europe Fund, managed by EQT, with existing investors participating and a planned investment from NVIDIA. Pigment said the new capital would accelerate work on its Graphite engine and its move toward autonomous planning—software designed to help teams build, analyze and update forecasts rather than merely display dashboards. The company reported exceeding $100 million in annual recurring revenue earlier in 2026. The financing highlights investor interest in applying AI to complex business planning and performance management.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
Pigment helps organizations model business performance, build forecasts and analyze planning scenarios. Its AI strategy aims to move toward more automated planning workflows, enabling teams to explore assumptions and update models as business conditions change.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐ฐ Financial planning: Build budgets and forecast business performance.
๐ Scenario analysis: Compare potential business outcomes.
๐ข Enterprise management: Connect planning across teams and departments.
๐ Forecast updates: Help planning teams respond to changing business assumptions.
๐ด. ๐ฎ๐ณ ๐ก๐๐๐ฅ๐ง ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐ฒ๐ ๐ฎ๐ป ๐๐ ๐ฎ๐ป๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป๐ฎ๐น-๐ง๐ต๐ถ๐ป๐ธ๐ถ๐ป๐ด ๐ ๐ผ๐ฑ๐๐น๐ฒ ๐ณ๐ผ๐ฟ ๐๐น๐ฎ๐๐๐ฒ๐ ๐ต–๐ญ๐ฎ
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
India's National Council of Educational Research and Training introduced an activity-based module on computational thinking and artificial intelligence for students in Classes 9–12. Developed with faculty from IIT Madras, IIT Jodhpur and NCERT, the 20-hour module is organized into five units and is designed for students without prior AI knowledge. It uses practical exercises to explain pattern recognition, machine learning and real-world AI applications, while also addressing safety, privacy, misinformation and fairness. The teaching approach encourages students to test systems and question AI-generated results rather than memorize definitions or accept model answers uncritically. The module aims to broaden AI literacy within school education.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
The module introduces students to computational thinking and the fundamentals of AI through practical activities. It also encourages critical evaluation of AI-generated information and develops awareness of privacy, misinformation, fairness and responsible technology use.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
๐ AI education: Introduce foundational concepts through classroom activities.
๐งฉ Problem-solving: Develop logical reasoning and computational thinking.
๐ Information literacy: Encourage students to check AI-generated answers.
๐ก️ Digital safety: Teach responsible use and awareness of AI limitations.
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๐ฅ ๐ง๐ฅ๐๐ก๐๐๐ก๐ ๐๐ ๐ก๐๐ช๐ฆ
๐ต. ๐ด๐ฒ ๐ข๐บ๐ฎ๐ป ๐๐ป๐ป๐ผ๐๐ป๐ฐ๐ฒ๐ ๐ฃ๐น๐ฎ๐ป๐ ๐ณ๐ผ๐ฟ ๐ฎ $๐ฏ ๐๐ถ๐น๐น๐ถ๐ผ๐ป ๐๐ ๐๐ฎ๐๐ฎ ๐๐ฒ๐ป๐๐ฟ๐ฒ
An Omani-led consortium announced plans for a $3 billion AI data-centre campus with capacity of up to 400 megawatts. Mubbadrah Investment is working with Sustopia Capital, Vela Tech and O2 Global Energy on a facility intended to serve hyperscale operators and support AI training and inference. O2 plans 400 MW of solar generation with battery storage alongside grid power. Crusoe has expressed interest as a potential customer, but that interest has not yet become a confirmed contract. The campus remains a proposed development, not operational computing capacity.
๐ญ๐ฌ. ๐ฐ๐ท ๐ฆ๐ผ๐๐๐ต ๐๐ต๐๐ป๐ด๐ฐ๐ต๐ฒ๐ผ๐ป๐ด ๐๐ฒ๐ด๐ถ๐ป๐ ๐ฎ ๐จ.๐ฆ. ๐ฃ๐๐๐ต ๐๐ผ ๐๐๐๐ฟ๐ฎ๐ฐ๐ ๐๐ ๐๐ป๐๐ฒ๐๐๐บ๐ฒ๐ป๐
South Chungcheong Governor Park Soo-hyun began a U.S. business trip on 25 October to attract investment and build connections with AI research and technology institutions. Planned stops include the New Jersey AI Hub associated with Princeton University, Microsoft and CoreWeave. The province says the visit supports its ambition to become a stronger AI and advanced-industry hub while helping local businesses expand overseas. Investment agreements with three global companies were scheduled for 27 October, so they had not yet been signed when the trip began. The visit is an investment campaign, not proof that the proposed investments are secured.
๐ญ๐ญ. ๐ ๐๐ผ๐๐ป๐๐ฟ๐ถ๐ฒ๐ ๐๐ฎ๐ฐ๐ฒ ๐๐ฟ๐ผ๐๐ถ๐ป๐ด ๐ฃ๐ฟ๐ฒ๐๐๐๐ฟ๐ฒ ๐๐ผ ๐๐ต๐ผ๐ผ๐๐ฒ ๐๐ฒ๐๐๐ฒ๐ฒ๐ป ๐จ.๐ฆ. ๐ฎ๐ป๐ฑ ๐๐ต๐ถ๐ป๐ฒ๐๐ฒ ๐๐ ๐๐ฐ๐ผ๐๐๐๐๐ฒ๐บ๐
Countries are increasingly trying to benefit from both U.S. and Chinese AI ecosystems without being forced into a single technological bloc. The U.S.-led Pax Silica initiative emphasizes advanced chips, trusted supply chains and technology partnerships, while China's World AI Cooperation Organization promotes wider AI access and cooperation. Kazakhstan has participated in both tracks, and countries including Indonesia are exploring ways to benefit from both. The competition now reaches beyond models into computing infrastructure, international standards and long-term technology dependence, creating difficult choices for countries seeking affordable access and strategic flexibility.
๐ญ๐ฎ. ๐ณ️ ๐ฆ๐๐๐ฑ๐ ๐๐ถ๐ป๐ฑ๐ ๐ฆ๐ต๐ฎ๐ฟ๐ฝ ๐ฃ๐ผ๐น๐ถ๐๐ถ๐ฐ๐ฎ๐น ๐๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐ ๐ถ๐ป ๐๐ผ๐ฟ๐ฐ๐ฒ๐ฑ-๐๐ต๐ผ๐ถ๐ฐ๐ฒ ๐๐ป๐๐๐ฒ๐ฟ๐ ๐๐ฟ๐ผ๐บ ๐๐ฟ๐ผ๐ธ ๐ฎ๐ป๐ฑ ๐๐ฃ๐ง
A study led by Cornell professor Tapan Parikh compared the Grok 4.7 and GPT-6.1 Sol developer APIs when forced to name candidates across nearly 500 U.S. midterm races. Grok recommended Republican candidates in 92% of qualifying answers, while GPT-6.1 Sol recommended Democrats in 97%; the models agreed in fewer than 10% of races. The test used a forced-choice prompt and web search, so it does not describe ordinary consumer-chatbot conversations or prove effects on voters. OpenAI disputed the test's relevance to ChatGPT's normal user experience.
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๐ ️ ๐๐ ๐ง๐ข๐ข๐๐ฆ ๐ข๐ ๐ง๐๐ ๐ช๐๐๐
๐ญ. ๐ค ๐๐ข๐ฆ๐ฆ ๐๐ผ๐ป๐๐ผ๐น๐ฒ
An open-source desktop workspace for running always-on AI agents with browser access, terminal, code editor and plugins. Available for macOS, Windows and Linux.
๐ฎ. ๐ง ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ
A shared-memory layer designed to help compatible AI assistants and coding agents retain and reuse context across tools such as ChatGPT, Claude and Cursor.
๐ฏ. ๐ง ๐ฌ๐ผ๐๐ฟ๐ฐ๐ฎ๐๐ ๐ฎ.๐ฌ
A Telegram-based podcast assistant that helps users manage feeds, summarize episodes, jump to timestamps and ask questions about podcast content.
๐ฐ. ๐บ️ ๐ฆ๐ฝ๐ฒ๐ฐ๐๐ถ๐ด๐ต๐
Turns a codebase into a product-oriented map of features and user journeys, helping teams understand functionality and communicate release updates.
๐ฑ. ๐ป ๐๐น๐ฎ๐บ๐ฎ ๐ช๐ถ๐ป๐ฑ๐ผ๐๐
A Windows 11 application for running local AI models, with an OpenAI-compatible API for integrating supported local models into other applications.
๐ฒ. ๐ ๐ฃ๐ฎ๐ฝ๐ฒ๐ฟ๐บ๐ฎ๐ฟ๐ธ ๐๐ฎ๐๐ฎ ๐ฅ๐ผ๐ผ๐บ๐
A platform for secure, branded document-sharing spaces used in fundraising, mergers and acquisitions, and due diligence, with developer-oriented integrations.
๐ณ. ☁️ ๐ฃ๐ผ๐น๐๐น๐ฎ๐ป๐ฒ ๐ณ๐ผ๐ฟ ๐๐น๐ผ๐๐ฑ๐ณ๐น๐ฎ๐ฟ๐ฒ
An AI-assisted incident-response tool for Cloudflare environments that helps investigate issues across Workers, databases, queues and storage, trace potential causes, and propose fixes.
๐ด. ๐️ ๐๐๐ด๐ฒ๐น ๐๐ฎ๐ฟ
A speech-recognition tool for voice agents that can ask callers to clarify uncertain words, helping reduce misunderstandings in spoken interactions.
๐ต. ๐ ๐๐ผ๐๐ ๐
A review workspace for Markdown specifications used by AI coding agents, helping developers inspect and refine plans before or during implementation.
๐ญ๐ฌ. ๐ง๐ป ๐๐๐ฅ๐จ๐ก๐๐ข๐๐ ๐ญ.๐ฑ.๐ฐ
An AI development environment aimed at coding agents building applications and 3D games, with a native 3D engine and workflows for testing and fixing projects.
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๐ฏ ๐ฃ๐ฅ๐ข๐ ๐ฃ๐ง ๐ข๐ ๐ง๐๐ ๐ช๐๐๐
๐งฉ ๐ง๐ต๐ฒ ๐ฅ๐ฒ๐๐ฒ๐ฟ๐๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ: ๐๐ถ๐๐ฐ๐ผ๐๐ฒ๐ฟ ๐๐ผ๐ ๐๐ป๐ ๐๐ฝ๐ฝ ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ ๐๐๐ถ๐น๐
๐ก WHAT THIS PROMPT DOES
Ever used an app and wondered how one of its features works behind the scenes? This prompt helps you break down an observed feature into its interface, behaviour, data flow, architecture and implementation steps—so you can build your own version using your preferred technology stack.
๐ ๐ง๐๐ ๐๐ข๐ฃ๐ฌ-๐๐ก๐-๐ฃ๐๐ฆ๐ง๐ ๐ฃ๐ฅ๐ข๐ ๐ฃ๐ง
Act as a senior software architect and product analyst. Help me understand how to build a feature inspired by an existing app, using only the information I provide.
MY INPUTS:
App or product: [Enter the app name]
Feature to analyse: [Describe the feature]
My observations: [Explain what happens when you use it]
Screenshots or UI description: [Upload screenshots if supported, or describe the interface]
My technology stack: [React, Python, Flutter, etc.]
My experience level: [Beginner / Intermediate / Advanced]
My goal: [Learning / Prototype / Production-ready implementation]
YOUR TASK:
Explain the feature's purpose and the problem it solves.
Break it into visible UI elements, user actions, application logic, and required data.
Describe the likely data flow from user action to displayed result. Clearly label anything that cannot be confirmed from my observations.
Propose a simple architecture suitable for my technology stack.
Divide implementation into small, ordered development tasks.
Identify edge cases, loading states, errors, and basic security considerations.
Create a test checklist to verify the feature works correctly.
Suggest a minimal first version and explain what can wait until later.
RULES:
Never claim to know the original app's private source code or internal architecture.
Separate observed behaviour from hypotheses and recommendations.
Do not invent APIs, database schemas, or integrations as confirmed facts.
Prefer original implementation choices rather than copying proprietary code, branding, or protected assets.
Keep the solution appropriate to my experience level.
If critical details are missing, ask focused questions before making major assumptions.
OUTPUT:
Feature Breakdown → Proposed Architecture → Development Roadmap → Edge Cases → Test Checklist → MVP Scope.
Finish with the single best first task I should complete.
๐ TRY IT WITH THIS EXAMPLE
App type: Task management
Feature: Calendar-based task scheduling
Observed behaviour: A user selects a date, adds a task and sees it on the calendar.
Technology stack: React + TypeScript
Experience level: Beginner
Goal: Working prototype
Your challenge: Use the prompt to turn one feature you admire into a clear, original implementation plan.
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๐ฌ ๐๐๐ก๐๐ ๐ง๐๐ข๐จ๐๐๐ง
AI is moving beyond chatbots. It's becoming infrastructure for research, software development, education, business operations and creative work.
The real advantage won't come from trying every new tool. It will come from understanding which tools solve meaningful problems—and learning how to use them well.
Stay curious. Build boldly. Verify before you believe.
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