5 Reasons Behind Enterprise #AI Failures by Ashwin Gaidhani @Forbes Learn more: bit.ly/41evPKx #ArtificialIntelligence #MachineLearning #ML #DL
→ View original post on X — @ronald_vanloon, 2026-04-07 20:58 UTC

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5 Reasons Behind Enterprise #AI Failures by Ashwin Gaidhani @Forbes Learn more: bit.ly/41evPKx #ArtificialIntelligence #MachineLearning #ML #DL
→ View original post on X — @ronald_vanloon, 2026-04-07 20:58 UTC
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Salesforce is looking to Slackbot to help it solve the SaaSpocalypse puzzle https://t.co/XZRx45t2A1 pic.twitter.com/PaR2hQHGcY
— Craig Brown, PhD (@craigbrownphd) 7 avril 2026
Salesforce is looking to Slackbot to help it solve the SaaSpocalypse puzzle go.theregister.com/feed/www.…

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AI is moving fast. The people building it move faster. If you’re working on models, infra, or agents—and care about real performance, not just hype—you should be in the SambaNova community. What you’ll get: Early access to what we’re building Real conversations with

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This isn’t theoretical. We’re already seeing AI integrated into networks to support real-time decisions, automation, and new services. The shift is happening now. See the post: ⏩ linkedin.com/posts/haroldsin… #MWC26 @SoftBank @SoftBank_RandD @ericsson
→ View original post on X — @haroldsinnott, 2026-04-07 20:04 UTC
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Levangie Labs. https://
levangielabs.com Unfortunately mostly for enterprises right now. @blevlabs is working on bringing it to more people.

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BREAKING: Tesla has officially released FSD V14.3 I'm downloading it in my Model Y right now. Here's everything that's new: • Improved parking location pin prediction, now shown on a map with a P icon. • Increased decisiveness of parking spot selection and maneuvering. • Rewrote the Al compiler and runtime from the ground up with MLIR, resulting in 20% faster reaction time and improving model iteration speed. • Enhanced response to emergency vehicles, school buses, right-of-way violators, and other rare vehicles. • Mitigated unnecessary lane biasing and minor tailgating behaviors. • Improved handling of small animals by focusing RL training on harder examples and adding rewards for better proactive safety. • Improved traffic light handling at complex intersections with compound lights, curved roads, and yellow light stopping – driven by training on hard RL examples sourced from the Tesla fleet. • Upgraded the Reinforcement Learning (RL) stage of training the FSD neural network, resulting in improvements in a wide variety of driving scenarios. • Upgraded the neural network vision encoder, improving understanding in rare and low-visibility scenarios, strengthening 3D geometry understanding, and expanding traffic sign understanding. • Improved handling for rare and unusual objects extending, hanging, or leaning into the vehicle path by sourcing infrequent events from the fleet. • Improved handling of temporary system degradations by maintaining control and automatically recovering without driver intervention, reducing unnecessary disengagements. Upcoming Improvements: • Expand reasoning to all behaviors beyond destination handling. • Add pothole avoidance. • Improve driver monitoring system sensitivity with better eye gaze tracking, eye wear handling, and higher accuracy in variable lighting conditions.
→ View original post on X — @scobleizer, 2026-04-07 19:19 UTC

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Anthropic's Head of Growth: "70% of what I spend my time on is what we internally refer to as 'success disasters.' Where things have gone so well that other things are breaking.
— Lenny Rachitsky (@lennysan) 7 avril 2026
It's funny, because all the charts are green and fully up and to the right, but it can be quite tough… https://t.co/dnF0CeuudT pic.twitter.com/1Om58KMZny
Anthropic's Head of Growth: "70% of what I spend my time on is what we internally refer to as 'success disasters.' Where things have gone so well that other things are breaking. It's funny, because all the charts are green and fully up and to the right, but it can be quite tough
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Appreciate the feedback. Since we introduced Claude Code at Anthropic, engineering velocity has increased hundreds of %, and the rate at which it is increasing is itself accelerating. The velocity is very much not performative — we're actively trying to figure out how to