๐๐ก๐ฉ๐-๐๐-๐๐๐ฅ๐๐๐ - ๐ฎ๐ณ ๐ก๐ฉ๐๐๐๐ ๐ฎ๐ป๐ฑ ๐๐๐ง ๐ ๐ฎ๐ฑ๐ฟ๐ฎ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ ๐ฎ ๐ฎ.๐ด-๐ง๐ฟ๐ถ๐น๐น๐ถ๐ผ๐ป-๐ง๐ผ๐ธ๐ฒ๐ป ๐๐ฎ๐๐ฎ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐ป๐ฑ๐ถ๐ฎ๐ป ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐
๐๐ก๐ฉ๐-๐๐-๐๐๐ฅ๐๐๐
๐ฐ ๐๐ ๐ก๐๐ช๐ฆ
1. ๐ฎ๐ณ ๐ก๐ฉ๐๐๐๐ ๐ฎ๐ป๐ฑ ๐๐๐ง ๐ ๐ฎ๐ฑ๐ฟ๐ฎ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ ๐ฎ ๐ฎ.๐ด-๐ง๐ฟ๐ถ๐น๐น๐ถ๐ผ๐ป-๐ง๐ผ๐ธ๐ฒ๐ป ๐๐ฎ๐๐ฎ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐ป๐ฑ๐ถ๐ฎ๐ป ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
NVIDIA, working with IIT Madras and AI4Bharat, introduced plans for a synthetic pretraining corpus spanning 22 Indian languages, totalling approximately 2.8 trillion tokens. The accompanying open generation recipe documents a GPU-accelerated workflow for translating curated English material into Indian languages, assessing quality and repairing weaker translations. The corpus includes native-script data for all 22 languages and romanised versions for 12. The full training dataset is described as forthcoming, so it should not yet be represented as a completed public download. The effort addresses a key bottleneck for Indian-language AI: insufficient high-quality training data.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
The pipeline combines translation, automated quality scoring and targeted repair while retaining links to source documents. Developers can adapt the workflow to new corpora and inspect training examples, rather than relying entirely on a fixed, opaque dataset.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
Indian-language AI: Build models that work with more regional languages.
Education: Develop multilingual learning assistants.
Public services: Improve access to digital services in local languages.
Research: Create and evaluate training datasets for language technology.
2. ๐ฌ ๐๐๐ฅ๐ก ๐ฎ๐ป๐ฑ ๐ ๐ถ๐๐๐ฟ๐ฎ๐น ๐๐ผ๐ถ๐ป ๐๐ผ๐ฟ๐ฐ๐ฒ๐ ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐๐น๐ฎ๐ฟ๐ฒ, ๐ฎ๐ป ๐๐ ๐ ๐ผ๐ฑ๐ฒ๐น ๐ณ๐ผ๐ฟ ๐ฃ๐ฎ๐ฟ๐๐ถ๐ฐ๐น๐ฒ ๐ฃ๐ต๐๐๐ถ๐ฐ๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
CERN and Mistral have signed a statement of intent to build Flare, a foundation model intended for scientific discovery, beginning with high-energy and collider physics. Unlike text-first chat models, Flare is planned to learn from billions of simulated particle collisions, using CERN’s physics expertise, scientific datasets and large-scale computing. The collaboration brings Mistral’s model-development capabilities into a research environment shaped by the Large Hadron Collider. The project remains a development partnership, not a released model, and no public launch timetable has been confirmed. Its longer-term goal is to create methods useful across the wider scientific community.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
Flare is intended to analyse patterns in simulated collision data and help researchers identify signals relevant to particle physics. It could eventually complement specialised detection algorithms, but its performance and scientific value will need validation as the model is developed.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
Particle physics: Analyse complex collision simulations.
Scientific discovery: Help researchers investigate patterns in experimental data.
Research computing: Apply foundation-model techniques to scientific workloads.
Future science: Explore similar approaches across other research disciplines.
3. ๐ค ๐ ๐ฒ๐๐ฎ ๐ ๐ฎ๐ธ๐ฒ๐ ๐ ๐๐๐ฒ ๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐ข๐ฝ๐ฒ๐ป ๐ช๐ฒ๐ถ๐ด๐ต๐๐ ๐๐๐ฎ๐ถ๐น๐ฎ๐ฏ๐น๐ฒ ๐ณ๐ผ๐ฟ ๐ ๐๐น๐๐ถ๐บ๐ผ๐ฑ๐ฎ๐น ๐๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
Meta’s Muse Spark Open became available as an open-weight multimodal model through a developer-focused release. The model has about 816 billion total parameters, with around 42 billion active per token, and supports text, image, audio and video inputs with a context window of up to one million tokens. Its training emphasis is agentic coding: navigating codebases, editing and debugging code, using tools and handling long multi-step work. The release includes guidance for inference and fine-tuning across multiple runtimes. Its large size means local deployment still requires substantial memory or suitable quantised configurations; open weights do not make it lightweight.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
It can combine text, image, audio and video context while performing multi-step coding tasks, including tool calls and self-correction. Developers can inspect, fine-tune and deploy the weights, subject to hardware requirements, runtime support and the model licence.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
Software engineering: Assist with code generation and debugging.
Multimodal agents: Interpret screenshots, audio and video alongside instructions.
Research: Experiment with model behaviour and fine-tuning.
Private infrastructure: Evaluate self-hosted deployments where hardware permits.
4. ๐ ๐๐ต๐ถ๐ป๐ฎ ๐ฃ๐ฟ๐ผ๐ฝ๐ผ๐๐ฒ๐ ๐๐ ๐ฝ๐ผ๐ฟ๐ ๐๐ผ๐ป๐๐ฟ๐ผ๐น๐ ๐๐ผ๐๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฎ๐ฏ ๐๐-๐ฅ๐ฒ๐น๐ฎ๐๐ฒ๐ฑ ๐ง๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
China’s commerce and science authorities opened consultation on a proposed revision to the country’s technology export catalogue, identifying 23 AI-related technologies for possible inclusion. The list spans large models, AI agents, computing clusters and information-processing technologies. Examples include AI-assisted vulnerability discovery and technology for generating or mutating malicious code, which fall into different proposed control categories. The announcement does not mean all AI models or AI services are being banned from overseas use; the draft distinguishes technical transfers from API, app, web and SaaS access. The proposal marks a new step in China’s oversight of cross-border AI technology transfers.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
The proposed controls would allow authorities to review or restrict cross-border transfers of certain advanced AI techniques, especially those with security implications. Their final scope depends on the consultation and formal revisions, not a blanket export ban on AI products.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
Export compliance: Assess whether technical transfers require registration or authorisation.
AI development: Review international model-development arrangements.
Cybersecurity: Consider controls on sensitive AI-enabled security technologies.
International collaboration: Plan research partnerships around applicable export rules.
5. ๐ ๐๐ ๐ ๐๐ต๐ถ๐ฒ๐ณ ๐ช๐ฎ๐ฟ๐ป๐ ๐๐ ๐ฅ๐ถ๐๐ธ๐ ๐๐ผ๐๐น๐ฑ ๐ข๐๐๐๐ฒ๐ถ๐ด๐ต ๐๐ฒ๐ป๐ฒ๐ณ๐ถ๐๐ ๐ช๐ถ๐๐ต๐ผ๐๐ ๐ฆ๐ฎ๐ณ๐ฒ๐ด๐๐ฎ๐ฟ๐ฑ๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
At the IMF and World Bank annual meetings in Bangkok, IMF Managing Director Kristalina Georgieva warned that AI’s risks could outweigh its benefits without safeguards and an ethical framework. She called for industry self-regulation alongside government engagement and public input, noting that access to electricity and digital infrastructure is necessary for broader benefits. Other participants raised concerns about uneven economic gains, job displacement and the possibility that heavy investment in AI infrastructure could disappoint. The discussion reflects a challenge: converting rapid technical progress into productivity gains while preparing for unintended consequences, rather than announcing a new regulation or binding standard.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
The remarks do not create a regulatory mechanism, but they frame priorities for AI governance: proportionate safeguards, shared standards, energy and digital access, and coordination between companies and governments. These priorities can shape future policy discussions.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
AI governance: Develop safeguards proportionate to potential risks.
Infrastructure planning: Address electricity and connectivity requirements.
Workforce planning: Prepare for possible job displacement and changing skills.
Business strategy: Evaluate AI investments against realistic productivity benefits.
6. ๐งฉ ๐๐๐ฆ๐ฐ๐ฎ๐น๐ฒ ๐๐ฎ๐๐ป๐ฐ๐ต๐ฒ๐ ๐๐ ๐ฆ๐๐ถ๐๐ฒ ๐๐ผ ๐๐บ๐ฝ๐ฟ๐ผ๐๐ฒ ๐๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐ป๐๐๐ฒ๐ฟ ๐๐ฐ๐ฐ๐๐ฟ๐ฎ๐ฐ๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
AtScale introduced AtScale for AI, a suite designed to help enterprises give AI systems consistent answers about business data. The offering is powered by its AI Computation Engine and combines AI-assisted semantic modelling, question clarification and accuracy testing with governed computation. It connects natural-language questions to shared definitions of metrics such as revenue, margin and active customers, reducing the chance that different teams receive conflicting answers. AtScale Ava and ACE are available now, while CoModel and Canary are planned for later release. The launch targets a persistent enterprise problem: better language models cannot fix unclear business definitions on their own.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
The suite maps natural-language requests to governed metrics and calculations, helping agents query business data with shared definitions. Its clarification and testing capabilities are intended to surface ambiguity and measure answer accuracy before AI is used in operational decisions.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
Business analytics: Answer questions using consistent company metrics.
Finance: Improve the reliability of revenue and margin calculations.
Enterprise agents: Connect AI workflows to governed business information.
Decision-making: Identify inaccurate or ambiguous answers before acting on them.
7. ๐ก️ ๐๐ป๐๐ต๐ฟ๐ผ๐ฝ๐ถ๐ฐ ๐๐ถ๐๐ฒ๐ ๐๐ป๐ฑ๐ฒ๐ฝ๐ฒ๐ป๐ฑ๐ฒ๐ป๐ ๐๐๐ฎ๐น๐๐ฎ๐๐ผ๐ฟ๐ ๐๐ฒ๐ฒ๐ฝ๐ฒ๐ฟ ๐๐ฐ๐ฐ๐ฒ๐๐ ๐๐ผ ๐๐๐๐ฒ๐๐ ๐๐ ๐ฆ๐ฎ๐ณ๐ฒ๐๐
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
Anthropic announced that six employees from the nonprofit METR have been given employee-like access to its internal systems to assess frontier AI risks. The evaluators receive company hardware, office space and access to tools similar to those used by Anthropic's internal risk teams, with limited exclusions for customer data, privileged information and sensitive model-architecture details. METR will publish its findings about risk levels, incidents and practices without Anthropic holding a general veto, although narrow confidentiality redactions remain possible. Anthropic is also working with other evaluators as it develops a broader approach to independent AI safety oversight.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
Independent evaluators can examine model risks, review safeguards and assess how a company manages incidents. Their findings can provide outside scrutiny of safety practices that cannot be fully evaluated through public demonstrations or company statements alone.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
AI safety: Independently examine advanced model behaviour.
Risk assessment: Review safeguards and incident management.
Accountability: Publish external assessments of AI developers.
Governance: Strengthen oversight before powerful models are deployed.
8. ๐ง ๐ก๐ฉ๐๐๐๐ ๐ฅ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ ๐ก๐ฒ๐บ๐ผ๐๐ฟ๐ผ๐ป ๐ฏ.๐ฑ ๐ ๐ผ๐ฑ๐ฒ๐น๐ ๐ณ๐ผ๐ฟ ๐๐ ๐๐ด๐ฒ๐ป๐๐ ๐๐ฟ๐ผ๐บ ๐๐น๐ผ๐๐ฑ ๐๐ผ ๐๐ฑ๐ด๐ฒ
๐ฐ ๐ช๐๐๐ง ๐๐๐ฃ๐ฃ๐๐ก๐๐ !!
NVIDIA released three Nemotron 3.5 models intended to support AI agents from data-centre servers to local computers. The lineup includes the open 120-billion-parameter Super VL model, Bolt 74B-A9B designed for local execution within 24GB of memory, and a smaller Bolt E4B model planned for later release. The family uses a hybrid Mamba–Transformer architecture and targets tasks that require tool use, extended workflows and efficient inference. NVIDIA positions the models as an option for developers who want to customise or deploy AI across different environments. Performance and usability depend on hardware, model configuration and workload.
๐ง ๐ช๐๐๐ง ๐๐๐ก ๐๐ง ๐๐ข?
The models are designed to support reasoning, multimodal understanding and agentic workflows. Their different sizes allow developers to explore deployments ranging from larger server environments to memory-constrained local systems, with capabilities depending on the selected model.
๐ ๐ฅ๐๐๐-๐ช๐ข๐ฅ๐๐ ๐๐ฃ๐ฃ๐๐๐๐๐ง๐๐ข๐ก๐ฆ
AI agents: Automate multistep tasks using tools.
Software development: Assist with coding and technical workflows.
Local AI: Explore running models on compatible hardware.
Enterprise infrastructure: Choose models for different deployment requirements.
๐ฅ ๐๐จ๐ฅ๐ฅ๐๐ก๐ง ๐๐ ๐ก๐๐ช๐ฆ
9. ๐ ๐ฆ๐ฒ๐ฟ๐๐ถ๐ฐ๐ฒ๐ก๐ผ๐ ๐จ๐ป๐๐ฒ๐ถ๐น๐ ๐๐ ๐ฃ๐ฎ๐๐๐ฝ๐ผ๐ฟ๐ ๐๐ผ ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐๐-๐๐ด๐ฒ๐ป๐ ๐ฃ๐ฒ๐ฟ๐บ๐ถ๐๐๐ถ๐ผ๐ป๐
.png)
ServiceNow introduced AI Passport, a permission layer designed to control what AI agents can do throughout enterprise workflows. The solution enables organisations to verify the authorised scope of first-party and third-party agents, including agents that use Model Context Protocol servers. Permissions can be checked at different stages of a multistep workflow, while scoped delegation and revocation help administrators restrict an agent's authority. ServiceNow also expanded its Super Intelligence Control Tower to improve discovery, visibility and measurement of AI costs against business outcomes. The announcement focuses on preventive governance, rather than relying exclusively on audit logs after an agent has acted.
10. ⚖️ ๐จ๐ฆ ๐๐๐ฑ๐ถ๐ฐ๐ถ๐ฎ๐น ๐ฃ๐ฎ๐ป๐ฒ๐น ๐ข๐ฝ๐ฒ๐ป๐ ๐๐๐ฟ๐๐ต๐ฒ๐ฟ ๐ฅ๐ฒ๐๐ถ๐ฒ๐ ๐ผ๐ณ ๐๐-๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ๐ฑ ๐๐ฒ๐ฒ๐ฝ๐ณ๐ฎ๐ธ๐ฒ ๐๐๐ถ๐ฑ๐ฒ๐ป๐ฐ๐ฒ
At a meeting in Boston, experts from OpenAI, Meta and other fields warned a US federal judicial panel that AI-generated audio and video are becoming harder to distinguish from authentic evidence. The committee has been examining deepfake authentication for several years, but the latest discussion highlighted how quickly generation quality is improving. Participants also pointed to the lack of a universal watermark or detector that can reliably verify content across systems. Rather than immediately adopting a new AI-specific evidence rule, the committee appointed a subcommittee to study the problem further. The challenge is establishing reliable courtroom checks without delaying legitimate cases.
11. ๐ ๐๐ฏ๐ ๐๐ต๐ฎ๐ฏ๐ถ ๐๐ฟ๐ฒ๐ฎ๐๐ฒ๐ ๐ง๐๐ ๐ ๐๐น๐ผ๐ฏ๐ฎ๐น ๐๐ผ ๐๐ ๐ฝ๐ฎ๐ป๐ฑ ๐๐-๐ฃ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐๐ผ๐๐ฒ๐ฟ๐ป๐บ๐ฒ๐ป๐ ๐ฆ๐ฒ๐ฟ๐๐ถ๐ฐ๐ฒ๐
Abu Dhabi established TAMM Global, a company designed to license and adapt the emirate's AI-powered government-services platform for governments abroad. Instead of exporting a fixed application, the venture plans to offer customised versions aligned with each jurisdiction's laws, policies, public services and citizen needs. The existing TAMM interface connects more than 1,200 public and private services and is used by approximately 4.7 million people. Its workflows can assist with routine tasks such as vehicle registration, renewing documents and starting businesses. Governments adopting the platform would retain control of their citizens' data while gaining a model for consolidating digital public services.
12. ๐ฎ ๐ฑ๐ฒ๐ฐ๐ผ๐บ๐ฝ.๐ฑ๐ฒ๐ ๐๐ถ๐๐ฎ๐ฏ๐น๐ฒ๐ ๐๐๐ ๐ฃ๐๐ฏ๐น๐ถ๐ฐ ๐๐ป๐ฑ๐ฒ๐ ๐ฎ๐ ๐๐ ๐๐ฐ๐ฐ๐ฒ๐น๐ฒ๐ฟ๐ฎ๐๐ฒ๐ ๐๐ฎ๐บ๐ฒ ๐๐ฒ๐ฐ๐ผ๐บ๐ฝ๐ถ๐น๐ฎ๐๐ถ๐ผ๐ป
The maintainer of decomp.dev announced that the site's public index and intake for new game projects would be shut down after AI systems dramatically accelerated matching decompilation. The community site had tracked volunteer efforts to reconstruct game source code from compiled binaries. The administrator said hostile forks, rushed ports and leaderboard competition had made the index increasingly harmful to the community. Existing project pages and historical records are to remain accessible. The decision is not framed as a rejection of AI itself; rather, it reflects how automation can overwhelm volunteer coordination and lower the effort needed to reproduce complex work. It raises fresh questions about sustainability and abuse in open-source communities.
๐งฐ ๐๐ ๐ง๐ข๐ข๐๐ฆ
๐️ ๐ฆ๐ฎ๐ถ๐ฑ๐ง๐ฟ๐ฎ๐ฐ๐ฒ
A privacy-focused voice-memo app for Mac that turns recordings from your iPhone, Apple Watch, or audio files into searchable transcripts and notes. Choose local or external AI models, ask questions about past recordings, and trace answers back to the original audio.
๐จ๐ง๐ฆ ๐๐ฎ๐ฑ๐ฑ๐ ๐๐๐๐๐ฒ๐
An AI family-planning assistant that creates weekly briefings by connecting children’s needs, calendars, responsibilities, and unfinished tasks. It helps parents anticipate upcoming events and handle family commitments before they become last-minute stress.
๐ค ๐๐ณ๐๐ฒ๐ฟ๐ฝ๐ฎ๐ฟ๐๐
An AI meeting follow-up assistant that converts transcripts into ready-to-review emails, chat messages, calendar invitations, and CRM notes. It checks connected work tools for context and prepares relevant drafts for approval, helping teams turn meeting decisions into action.
๐ธ ๐ก๐ฎ๐๐ถ๐ด๐ฒ
An AI infrastructure tool that reduces AI-agent operating costs by routing suitable requests to smaller models, caching repeated reads, and trimming oversized tool outputs. It also helps developers set spending limits, detect runaway agent loops, and control sensitive actions.
๐ก️ ๐๐ถ๐๐ต๐๐๐บ๐ฎ๐ป
An open-source approval layer that gives teams control over consequential AI-agent actions. It intercepts tool calls, applies approval rules, and routes sensitive operations to human reviewers before agents can execute them.
๐ค ๐๐ฟ๐ฒ๐ฎ๐บ๐๐ฐ๐ฎ๐น๐ฒ ๐๐ฎ๐ฏ๐
A cloud inference platform built for physical AI and robotics. It helps robotics teams run and evaluate AI models remotely, optimizing compute, GPU execution, and network latency so robotic systems can move from experimentation toward real-world deployment.
๐️ ๐๐ฐ๐๐๐ฎ๐น๐น๐ ๐๐๐ธ๐ฒ๐ฑ
An AI-powered FAQ tool for Shopify stores that answers shoppers' questions using the store's products, policies, and existing pages. Frequently asked questions can become permanent FAQ entries, while merchants retain control over edits and unanswered queries.
๐ป ๐๐ผ๐ฑ๐ฒ๐๐ฟ๐ฎ๐ฏ
A local-first AI code-review application for macOS that uses your existing AI coding tools to inspect pull requests and uncommitted changes. It identifies potential bugs and risky code, explains reviewer feedback, and helps developers apply fixes while keeping source code on their machines.
๐ง๐ป ๐๐๐ฑ๐ถ๐บ๐ฎ๐๐ฒ
An AI desktop assistant featuring Arro, which works directly inside the applications you already use. It can understand screen context, guide you through unfamiliar tasks, perform repetitive computer workflows, and repeat demonstrated processes on demand or on a schedule.
๐ ๐๐๐ฟ๐ฎ๐ถ๐ป ๐ง๐ผ๐ผ๐น๐
An AI access-management tool that connects services such as Gmail, GitHub, Stripe, and Notion to compatible AI assistants through a shared interface. It centralizes permissions and activity logs so teams can control what their agents access without repeatedly sharing account credentials.
✨ ๐๐ ๐ฃ๐ฅ๐ข๐ ๐ฃ๐ง ๐ข๐ ๐ง๐๐ ๐๐๐ฌ
๐ง ๐ง๐๐ฟ๐ป ๐ฌ๐ผ๐๐ฟ ๐ฆ๐ฐ๐ฟ๐ฒ๐ฒ๐ป๐๐ต๐ผ๐๐ ๐๐ป๐๐ผ ๐ฎ ๐ฃ๐ฒ๐ฟ๐๐ผ๐ป๐ฎ๐น ๐๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ ๐ฆ๐๐๐๐ฒ๐บ ๐ฑ
Important Note: You’ll need to use the prompt below to get the complete result.
๐ช๐ต๐ฎ๐ ๐ง๐ต๐ถ๐ ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ผ๐ฒ๐
Your phone may be full of screenshots containing useful tips, recipes, study notes, project ideas, and things you planned to do later. This prompt helps you turn screenshot text or descriptions into an organized, searchable knowledge system with clear categories, useful summaries, and actionable next steps—without manually sorting everything from scratch.
๐ ๐ง๐ต๐ฒ ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐
Act as my personal knowledge organizer.
Help me turn the screenshot text or descriptions I provide into a structured collection of useful information that I can revisit and act on.
MY SCREENSHOT CONTENT:
[Paste extracted text from screenshots or describe each screenshot. Label them Screenshot 1, Screenshot 2, etc.]
MY MAIN PURPOSE:
[Study / Productivity / Business ideas / Recipes / Personal reference / Other]
PREFERRED ORGANIZATION:
[By topic / By project / By priority / By date]
Analyze the content and create:
1. CONTENT INDEX
Give each screenshot a number, a short descriptive title, and a category.
2. KEY TAKEAWAYS
Summarize the most useful information from each screenshot in 1–3 concise bullet points.
3. ACTIONABLE ITEMS
Identify specific tasks, ideas to try, or information worth saving for later. Separate actual instructions from your own suggestions.
4. DUPLICATES AND CONNECTIONS
Identify repeated information and related ideas across screenshots. Do not merge items unless their meaning genuinely overlaps.
5. SEARCHABLE KNOWLEDGE LIBRARY
Create a clean table with these columns:
Screenshot ID | Title | Category | Key Information | Relevant Keywords
6. NEXT STEPS
Recommend up to three useful actions based on my stated purpose. If the screenshots are reference material rather than tasks, suggest the best way to organize or revisit them.
RULES:
- Use only the information I provide.
- Never invent missing text or pretend to have seen screenshots that were not supplied.
- Mark unclear or incomplete content as “Needs review.”
- Preserve important numbers, names, instructions, and warnings accurately.
- Do not treat every saved idea as a task that must be completed.
- Keep the output concise, searchable, and easy to copy into a notes app or spreadsheet.๐งช ๐ง๐ฟ๐ ๐๐ ๐ช๐ถ๐๐ต
Paste the text from five screenshots containing Python coding tips, study notes, productivity advice, and project ideas.
Set your main purpose to Productivity and your preferred organization to By topic.
๐ฏ ๐ช๐ต๐ฎ๐ ๐ฌ๐ผ๐’๐น๐น ๐๐ฒ๐
A structured index of your saved information, concise summaries, searchable keywords, duplicate detection, and practical next steps. You can transfer the resulting table into your preferred notes app or spreadsheet to build a personal reference library that is easier to search and maintain.
Pro tip: If your AI tool supports image uploads, you can provide the screenshots directly instead of extracting their text manually.
Comments
Post a Comment