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  • Qwen 3.6 Plus Replaces Claude: Developer Switches After 90M Tokens
    Qwen 3.6 Plus Replaces Claude: Developer Switches After 90M Tokens

    By

    @clementdelangue

    –

    Its official. 90M tokens later. Qwen 3.6 Plus took all Opus tasks like a king! Since then my Claude usage has been 0. Not a single request! Not a single chat message. The best of it all, Qwen 3.6 Plus is behaving like SOTA models, with little to no errors on super complex, multi sub agents, multi-tasks, long-running tasks, you name it. You know what, I feel a little bad for Anthropic, it did help me build great tools such as truelens.tech that is changing the construction market in UK. If they did not do this super wrong move, I would still be there, so thank you @AnthropicAI I know there are 1000's of other amazing builders doing the exact same as we speak, but with other models. The point is that Anthropic jus did what it was not supposed to do, pushing those builders away, and once they run their own local models, those companies wont see a single cent anymore.

    → View original post on X — @clementdelangue, 2026-04-05 15:25 UTC

  • Google DeepMind Announced as Presenting Sponsor of AIE Europe
    Google DeepMind Announced as Presenting Sponsor of AIE Europe

    By

    @swyx

    –

    always wanted to do one of those “is it coachella” announcements – here is our designer’s take on it! AI Engineer (@aiDotEngineer) 🇬🇧 London is the birthplace of @GoogleDeepMind, and we're so honored to have them back as: Presenting Sponsors of this week's AIE Europe! DeepMind has pushed the AI frontier on every modality — from Gemini 3.1, to Embeddings 2, to Veo 3, to @NanoBanana Pro, and last far from least Gemma 4, byte for byte the most capable multimodal models in the world! Meet the team to catch up on everything GDM has shipped for AI Engineers in the past few months – from our keynoters Dr @RaiaHadsell (VP of Research and UK AI Ambassador) and @osanseviero (Lead AI DX), to returning speakers @DynamicWebPaige and @thorwebdev and @_philschmid, to researchers and engineers working on those exact products @sedielem, @tara_ojo, @FMuntenescu and more! — https://nitter.net/aiDotEngineer/status/2040806678849884422#m

    → View original post on X — @swyx, 2026-04-05 15:21 UTC

  • The End of the Org Chart in Agentic Enterprise Leadership

    By

    @nicochan33

    –

    The end of the org chart >> #Leadership in an agentic enterprise buff.ly/UHWMSTX #business #management #governance #organization #tech #CIO #CTO #CDO #CEO #digital #innovation #disruption #digitaltransformation #automation #AgenticAI #skills #talent #talentmanagement #jobs #futureofwork @CIOonline @BetaMoroney @ChuckDBrooks @MikeFlache @Khulood_Almani @IngridVasiliu @JonBelsher @Timothy_Hughes @NafisAlam @YuHelenYu @RLDI_Lamy @MCLynd @OlKonol_oa @RigneySec @BenRothke @HWillert @AndrewinContact @TerenceLeungSF @Shi4Tech @enilev @Nicochan33 @SabineVdL @bimedotcom @IanLJones98 @FrRonconi @RiccardoBua @Eli_Krumova @NYIke @HaroldSinnott @SallyEaves @GlobalIQX @AntGrasso @AkwyZ @DinisGuarda @PVynckier @YvesMulkers @HaleChris @BillMew @RobMay70 @NigelTozer @m49D4ch3lly @Ronald_vanLoon @jeancayeux @NevilleGaunt @asokan_telecom @cybersecboardrm @RVP @QuePasaChico

    → View original post on X — @nicochan33

  • GPT-5.5 Spud Incoming: OpenAI’s Make-or-Break Moment
    GPT-5.5 Spud Incoming: OpenAI’s Make-or-Break Moment

    By

    @montreal_ai

    –

    So GPT-5.5 "Spud" is coming. OpenAI finished pretraining around March 24. Altman called it "a very strong model that could really accelerate the economy" and Brockman said there are two years of research inside it. From what we're hearing, Spud is not just another update. It's supposedly a completely new base model with native multimodality, stronger agentic capabilities, and better understanding of what users actually want without having to over-explain. They even shut down Sora to put everything into this. Also worth noting that Q2 2026 is looking insane. Claude Mythos, DeepSeek V4, Grok 5 are all expected around the same time. This is going to be the most competitive quarter in AI history. Of course, take all of this with a grain of salt. Most of it is still unverified leaks and rumors. But here's my honest thought. GPT-5.5 is clearly one of the most anticipated models this year and this feels like a make-or-break moment for OpenAI. Whether Spud succeeds or not, I really hope they reconsider how they treat their models and find a way to replace or soften their approach to CoT monitoring. What made 4o special wasn't just performance. It was the way it felt human.

    → View original post on X — @montreal_ai, 2026-04-05 15:04 UTC

  • Coding agents outperform long-context models
    Coding agents outperform long-context models

    By

    @godofprompt

    –

    BREAKING: Duke researchers just proved that coding agents are better at processing long documents than models with million-token context windows. > Not because of longer context. Because grep and sed are better retrieval tools than attention. > +17.3% average improvement

    → View original post on X — @godofprompt

  • Robots are gradually entering a major sector

    By

    @kimmonismus

    –

    source: teddit.net/r/robotics/comments/1scemxg/robots_are_starting_to_enter_one_of_the_most/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button [Translated from EN to English]

    → View original post on X — @kimmonismus, 2026-04-05 14:47 UTC

  • Terminal Features Now Standard in AI Development

    By

    @anndylian

    –

    Terminal features have become a standard feature

    → View original post on X — @anndylian

  • AI Referees in Beach Volleyball: Enhancement or Soul Loss?

    By

    @ronald_vanloon

    –

    #AI Referees the Sand: Real-Time Beach Volleyball Analysis — Enhancing the Game or Changing Its Soul?
    by @measure_plan #ArtificialIntelligence #MachineLearning #ML

    → View original post on X — @ronald_vanloon

  • Modulate’s Velma API Achieves 98.9% Deepfake Detection Accuracy
    Modulate’s Velma API Achieves 98.9% Deepfake Detection Accuracy

    By

    @sumanth_077

    –

    Massive breakthrough in voice deepfake detection! @modulate_ai just released a deepfake detection API that topped @huggingface's leaderboard at 98.9% accuracy. Here's the problem with how most companies handle deepfake detection. They check the first 10 seconds of a call. If it passes, they assume the whole call is clean. Gate check. One scan. Done. Fraudsters know this. So they open the call with a real voice. Their own voice, a colleague, a quick recording. Pass the check. Then switch to the AI-generated clone mid-call. The system already gave them the green light. They're through. The fix is obvious. Monitor the entire call. Not just the opening. Not random spot checks. Every segment, continuously, in real-time. But that was too expensive. Until now. Velma is Modulate's real-time and batch deepfake detection API. Here's what changed. • Real-time streaming detection. Analyzes audio every 2 seconds during live calls. Catches mid-call voice switches instantly. • 120x cheaper than competitors. $0.25 per hour instead of $30-150. Now you can actually afford to monitor full conversations instead of spot-checking. • Only needs 2.5 seconds of audio. Faster detection, works with short segments. • 98.9% accuracy, ranked first on HuggingFace. Lower error rate than models 10x larger. First 1000 API credits are free. I've shared the link in the replies!

    → View original post on X — @sumanth_077, 2026-04-05 14:09 UTC

  • Huawei’s Smart Noise Scheduler Improves Diffusion LLM Logic Learning
    Huawei’s Smart Noise Scheduler Improves Diffusion LLM Logic Learning

    By

    @jiqizhixin

    –

    What if Diffusion LLMs learned logic more efficiently? Huawei's Noah's Ark Lab proposes a breakthrough: their "smart noise scheduler" uses priority masking to focus training on only information-dense data, making DLLMs master core reasoning and structure. This boosts average accuracy by 4% on Code & Math reasoning, beating uniform baselines and unlocking DLLM potential. Mask Is What DLLM Needs: A Masked Data Training Paradigm for Diffusion LLMs Paper: arxiv.org/abs/2603.15803 Dataset: huggingface.co/datasets/malr… Our report: mp.weixin.qq.com/s/1yTd36hev… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-05 14:05 UTC