@TechEqualizer

In a digitally transforming world, technology is democracy. This channel spread knowledge. Stay tuned. #DigitalTransformation

Joined December 2016
Learning styles vary: some prefer visual aids, others learn through action, some absorb through listening, while others grasp concepts best through reading and writing, shaping unique learning paths. What about you? #Learning #PersonalGrowth
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If you think AI agents can negotiate without human direction and oversight, good luck. Autonomy without boundaries is called loss of control. Let agents move, but keep the mandate visible and human intervention ready when the negotiation moves beyond it. Microblog @antgrasso Take a procurement agent negotiating with the agent of a supplier. It can adjust delivery dates or discounts within an agreed mandate. Fine. But the mandate is only the starting point. If the negotiation moves toward a condition the agent was not authorized to accept, someone needs to see it and be able to take over. That does not mean a human must approve every move. Otherwise, autonomy disappears. It means the organization knows where the agent can act and when human judgment must return. After the agreement, people should also be able to reconstruct why it was reached. Let the agents negotiate. Keep humans in command of the authority they delegate. Autonomy should reduce human intervention, not human responsibility. #AIAgents #AIGovernance #AgentOps
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🤖 Agentic AI isn’t just another AI upgrade — it could fundamentally change how organisations operate. Most businesses are already familiar with AI that responds to prompts, generates content or helps employees complete individual tasks. Agentic AI takes things much further. AI agents can potentially work towards goals, make decisions, use tools, coordinate tasks and execute entire workflows with far greater autonomy. ⚡ That means organisations need to think beyond simply giving employees access to AI tools. They need to consider how processes are designed, where humans remain involved, what decisions agents can make and how those systems are governed. The shift could be significant: from people using AI to organisations working alongside AI agents. Is your organisation preparing for that change? 👇 #AI #AgenticAI #AIAgents #ArtificialIntelligence #AIStrategy #FutureOfWork #BusinessAI #Automation #DigitalTransformation #AILeadership #FutureOfAI #Technology
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Robot Talk Episode 139 👇 #Robot sensing and manipulation 👇 robottalk.org/2026/06/05/epi… @Robohub w/ Maria Koskinopoulou, Assistant Professor in #Robotics & Computer Vision at @HeriotWattUni #WomenInTech #AI Cc @efipm @HaroldSinnott @EvanKirstel @RLDI_Lamy @DeepLearn007 @ahier
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An agent hands you 600 recommendations. You have one developer and two days. Which fix goes first? We asked two SEO and two social media experts the same question: What insights are agents actually good at producing, and where do they fall short? ⚡ Turns out it takes two to draw the line: agents are good at finding patterns, and weak at deciding which ones matter. → @LindaGrass0 says agents can make intelligence abundant, but abundance isn’t wisdom. The insights that matter still need context, judgment and questions an agent wouldn’t think to ask. → @CrystalontheWeb says the quality of an agent’s insights starts with the data you give it. Get data salience right first. → @Netanel relies on agents to spot technical issues and patterns. Deciding which fix deserves the developer’s two days still takes expertise and an understanding of the business. → Annie-Mai Hodge uses agents as a sounding board and for drafts, workflows and trend monitoring. But they struggle with culture: memes, nuance and the years of lore behind a certain turn of phrase. All four answers below 👇
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Rising energy demand is half the equation. The harder question is whether infrastructure and capacity can grow fast enough to deliver energy where and when economies need it. ADIPEC 2026 puts this question on the table. Join > adipec.com @ADIPECOfficial Partner. Adding supply without expanding the infrastructure that delivers and manages energy can create bottlenecks. Grids, storage, logistics and digital infrastructure have to keep pace with demand. Investment and execution do too. For me, this is the real infrastructure question at ADIPEC 2026: can industry, government, finance and technology align around delivery? More energy matters. So does the system that gets it where demand is growing. Energy. Intelligence. Impact. #ADIPEC2026 #ADIPEC #Energy #Infrastructure
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Quant connects blockchains with enterprise & legacy systems through Overledger, allowing developers to build multi-chain applications without relying on a single network. The QNT token is used to access the platform and support activities. Microblog @antgrasso #CryptoExplained
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With September 30 fast approaching, Federal agencies may still be refining their technology priorities. Start with the outcome you need to support, then choose the right path to get there. See how @lumentechco can help you connect modernization goals to procurement options: bit.ly/4hdBPL1 #MissionReadyWithLumen
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Deel links adding >$140M in ARR over 90 days without increasing headcount to its internal use of Akai. What interests me is how Akai learns the path through a workflow, including edge cases, and turns it into something teams can reuse and adapt.
🤝 Paid partnership
EXCITED TO LAUNCH: Akai (akai.run) Deel added >$140M ARR in 90 days without increasing headcount by automating~600 Full Time Employees' equivalent in work with Akai. Akai was an internal tool to automate our painfully repetitive operations in Finance, HR, Accounts Payable, and Compliance, etc. We never intended to make this a product. But we watched revenue per employee grow from $130K to $215K We built >8k agents that do the work of ~600 employees It had such a dramatic impact on our business that today we are launching it for everyone. How it works: Say you're automating payment reconciliation: 1. Record your screen while manually matching a messy transaction and Akai will capture your screen, voice, server requests 2. Akai will see that you pulled unformatted wire transfer info from an archaic bank portal, put it in some excel sheet, checked NetSuite invoices, payment history, and put a ticket on Zendesk 3. Akai reads between the lines and build a workflow + steps + conditional guardrails. It learns tacit edge cases, like resolving malformed invoice references without you writing a single regex 4. Simply connect NetSuite, your ledger, Zendesk, PSPs, and even legacy bank portals with zero API access 5. Run the workflow and tell it what to adjust in plain English: "strip slashes on wire memos and auto-apply partial payments." It adapts instantly 6. Once it works for you, add 100s of colleagues. Your entire payment ops team forks and extends the workflow for new PSPs, secondary ledgers, or regional settlement rules 7. We automated 85% of our payment reconciliation end to end, eliminating 500+ hours of soul-crushing manual grunt work every single week. Claude Code/Codex can't do this in multiplayer mode. Every person rebuilds the same skill from scratch in their own way. Deel built Akai to: 1. Understand backend operations edge cases (it had to work for our 7000 person team first) 2. Collaborative across 1000s of employees 3. Self-Learning from millions of runs 4. Optimises cost and gets cheaper every run We're so confident that we're announcing an Automation Guarantee: If our engineers can't automate a thousand of hours of work in your first 30 days, you get a full refund. Book a demo: akai.run if you're an exec at a company with hundreds of employees
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Short bursts of exercise can boost your cognition newscientist.com/article/258…
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You have heard that AI consumes energy. Fine. But what exactly is using that energy while you type a prompt and wait for the answer? What you see on screen is only the result, so the energy behind it goes beyond the few seconds spent generating the answer. Microblog @antgrasso There is no single energy cost hiding inside an AI answer. Some electricity is already being used before your prompt arrives. The infrastructure may be powered and ready to serve requests. Then you press Enter. The model has to process your prompt before it can answer. Longer inputs, especially when they include a lot of context, can require more computation. Then comes generation. The model produces the response step by step, using computation as the output grows. Longer responses generally require more work. The model is not working alone. Memory and networking keep data moving through the system, while cooling uses electricity as the hardware runs. Which part uses the most energy? There is no fixed answer. It depends on the model and the workload. Model size, prompt and response length, modality, and infrastructure efficiency can all change the total. That is why saying “one AI prompt consumes X” can hide more than it explains. The energy is in the whole slice. Like a cheesecake, you enjoy the result without necessarily thinking about what went into every layer. #ArtificialIntelligence #EnergyEfficiency
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AI can change faster than an enterprise can redesign a process. That makes repeatability a strategic advantage: the less you rebuild from scratch, the more likely innovation is to reach production before the next wave arrives.
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BrainBody-LLM algorithm helps robots mimic human-like planning and movement buff.ly/aAjD48E via @techxplore_com #MachineLearning #Robotics #AI #GenAI Cc @pierrepinna @HaroldSinnott @dinisguarda @JoannMoretti @ahier @Ym78200
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The Frameworks That Were Doing “Graph Engineering” Before It Had a Name buff.ly/Ykn8ftI @DataScienceDojo #AgenticAI #GenAI Cc @RLDI_Lamy @FrRonconi @aure79lien @YvesMulkers @rautsan @IanLJones98
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Ethereum is a programmable blockchain where Smart Contracts power decentralized applications, decentralized finance (DeFi), and tokenized assets. ETH secures the network and pays for transactions across its global ecosystem. Microblog @antgrasso #Ethereum #CryptoExplained
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Progress starts when you use what you already have instead of waiting for everything to feel ready. One simple step creates feedback, and feedback helps you adjust, learn, and keep moving with more confidence. #PersonalGrowth
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Digital early-warning systems could help authorities act before disaster strikes. The EU-funded @GOBEYONDEU develops platforms combining real-time data and tailored risk assessments to show where hazards may occur and who could be affected. #WeekendRead: link.europa.eu/X7Gm6P
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You and your AI do not need to split the work 50/50; what matters is the right contribution at the right moment, not an equal share. Good collaboration is not measured by symmetry, but by whether each side contributes what the task needs when it needs it. Microblog @antgrasso In practice, the balance can shift from one task to the next. A procurement team may let AI scan thousands of supplier records and flag unusual changes. Most of the volume sits with the machine. Then one case reaches a buyer who knows that a supplier is going through a temporary production change. Context changes the interpretation, and one human judgment can outweigh hours of machine processing. AI may then return to the workflow and handle the next batch. No symmetry required. Responsibility follows a different rule. If an AI-supported recommendation affects a supplier decision, the organization still owns the outcome. So do not measure collaboration by how much work each side does. Ask whether AI is handling the work it can do well and whether people enter where judgment is needed. A 50/50 split looks neat on a slide. Real collaboration follows what the work requires. #HumanAICollaboration #FutureOfWork
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Oh boy! Changes are probably in order.
Embarrassing product by Oklahoma!
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When AI's confidence outweighs its common sense 😂 #extraspacestorage #storageunit
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