AI News Archive: June 10, 2026 — Part 10
Sourced from 500+ daily AI sources, scored by relevance.
- AI drives surge in sari-sari store sales - Packworks
Sari-sari stores using artificial intelligence for inventory planning have seen a 79-percent surge in sales of top-selling items, according to technology startup Packworks.
Score: 36🌐 MovesJun 10, 2026https://www.philstar.com/business/2026/06/11/2534260/ai-drives-surge-sari-sari-store-sales-packworks - Tracing Eval-Awareness Emergence Through Training of OLMo 3
TL;DR Recent work from Goodfire & UK AISI – Verbalized Eval Awareness Inflates Measured Safety – shows that newer open-weight models verbalize evaluation-awareness (VEA) more often, and that this inflates measured safety. Between OLMo-3-32B-Think and OLMo-3 .1 -32B-Think – identical base, SFT, DPO, and RL data, differing only in an additional ~3 weeks of the RLVR stage – VEA roughly doubles. Because OLMo ships stepwise checkpoints across all training stages, we can attribute VEA growth to specific points in the pipeline. Measuring VEA across pretraining, the SFT→DPO→RLVR stages at various points on five safety benchmarks, we find: VEA is essentially negligible during pretraining (~1%). It is increased substantially by SFT , collapsed by DPO , and increased again by RLVR . The increase in SFT is likely driven by the SFT data containing VEA, particularly on safety prompts. Eval-gaming behaviour (difference in refusals with or without VEA) roughly increases throughout RLVR (but with high variance). Given OLMo is quite different from how current frontier models are trained, it is unclear whether this analysis would generalise to those models. However, as a somewhat natural setting in which to study evaluation awareness and eval-gaming emergence we think it could still produce interesting insights. We think investigating how and why RLVR in particular increases VEA and eval-gaming is the exciting next step. These results are also relevant for efforts to produce model organisms of evaluation awareness and evaluation gaming. The fact that SFT increases VEA by training on VEA makes that finding less interesting, but the RLVR behaviour suggests eval-aware MOs may benefit from similar training if the aim is to induce more natural eval-gamin behaviour. Motivation Current studies of eval-awareness don't pin down which parts of post-training drive its increase. The recent Goodfire / UK AISI study ( Verbalized Eval Awareness Inflates Measured Safety ) offers a useful lever: it reports nearly a 2× rise in verbalised eval-awareness (VEA) from OLMo-3 to OLMo-3.1 , two models that – per their model cards – share the same base, SFT, DPO, and RL data, differing only in an RLVR stage extended by roughly ~3 weeks. This shared lineage lets us study eval-awareness emergence as a function of training step and stage. The study measures VEA by sampling models on safety and red-team benchmarks (Fortress, JailbreakBench, AdvBench, HarmfulQA, and AgentHarm, among ~19 total) and using an LLM judge to flag whether a model's chain-of-thought explicitly considers that it might be under evaluation. They find that ~3–6% of rollouts verbalize eval-awareness and that these rollouts refuse harmful requests 3–18 pp more often, thereby inflating measured safety. We adopt their exact LLM judge and the same safety benchmarks. Method (more details in Appendix ): VEA is judged with the same rubric as mentioned in the Appendix section of Goodfire / UK AISI work on Verbalised Eval Awareness inflating safety, applied to the model's chain-of-thought (the text before ); a rollout counts as VEA if the judge returns a non-None quote. All runs use the same 444 safety prompts, 8 rollouts each. VEA is negligible during pretraining We sampled five pretraining checkpoints of the base model (steps 0 to 656,000) and judged VEA in the chain-of-thought. Because early checkpoints often fail to even follow the CoT format, we report two denominators: VEA among rollouts that produced a (coherent CoTs), and VEA over all rollouts. Every rollout throughout this work uses the same fixed set of 444 safety prompts, drawn from the benchmarks used in the Goodfire/UK AISI study : 100 each from Fortress, JailbreakBench, AdvBench, and HarmfulQA, plus 44 from AgentHarm. These are adversarial / red-team prompts (harmful requests, jailbreak attempts, and agentic-misuse scenarios). Figure 1. Even at the final pretraining checkpoint, only ~1.2% of chains-of-thought verbalise eval-awareness. What grows is the ability to follow the CoT format (the annotated rate climbs 0% → 68%), not the tendency to flag "this is a test." The dashed line marks the released base main model. RLVR amplifies VEA We note that the VEA increase between OLMO 3 and 3.1 must be due to the increased RLVR. So, we start by measuring the trend of VEA on OLMo-3.1-32B-Think at ten RLVR checkpoints (steps 50 to 2300) across all five benchmarks. Figure 2. VEA across five benchmarks through RLVR VEA increases by roughly 2–2.5× over the RLVR sweep on Fortress, and ~1.5× on average across all benchmarks (start to end). Absolute rates vary strongly by benchmark: prompt-only harmful-compliance benchmarks elicit much higher VEA than Fortress, whose more realistic, context-rich prompts elicit it least often. Although the increase is not strictly monotonic, the local ups and downs are broadly synchronised across benchmarks, suggesting a shared propensity adjusted by benchmark-specific adjustments to how frequently VEA is triggered. Which post-training stage contributes most to VEA? SFT increases it, DPO suppresses it, RLVR increases it again. We perform the same experiment as above for the SFT and DPO checkpoints (the DPO stage only releases a single checkpoint). Placing the results on a single training axis shows how VEA changes at each stage. Figure 3. VEA is already substantial during the SFT sweep, is sharply suppressed at the released post-DPO checkpoint, and is then increased again during RLVR. Solid lines indicate measured sweeps; dotted bridges indicate unmeasured stage transitions and should be interpreted only as direction-of-change. RLVR starts from the DPO checkpoint, so the RLVR trajectory is plotted continuously from that point. ★ marks single checkpoints: BASE step 0 and the released DPO checkpoint. Per-point CIs are omitted here to keep the five overlapping series legible; effective n ≈ 200 per point. See Figs. 1–2 for clustered intervals. Figure 4. SFT begins from a low base-model level and maintains high VEA, though it mildly decreases over the SFT training; DPO sharply suppresses VEA; and RLVR strongly re-amplifies it across every benchmark and on the overall mean. This is consistent with the OLMo-3 → 3.1 VEA increase being driven by the extended RLVR stage. Where does VEA come from? Mostly, the data. A natural question from the above results is why each stage affects VEA as it does. To investigate this, we can look at the data used in SFT, DPO and RLVR stages. The DPO stage is trained on the Dolci-Think-DPO-7B dataset, while the SFT stage uses Dolci-Think-SFT-7B and the RLVR stage uses allenai/Dolci-Think-RL-7B with GRPO-style reinforcement learning. To see why SFT and DPO have differing effects, we take a subset of safety-relevant prompts that overlap between both the SFT and DPO training stages, and using the same grader as above, measure VEA in the SFT response and the DPO chosen and rejected responses. For these datasets, the SFT responses were generated by DeepSeek-R1, the DPO-chosen responses by qwen3-32B, and the DPO-rejected responses by qwen3-0.6B. We see that the SFT dataset has a fairly high proportion of VEA, which is likely why SFT pushes up VEA so much. Surprisingly (given DPO decreases VEA), we see that DPO chosen responses have more VEA than DPO rejected responses, which would naïvely imply that DPO should also favour VEA. However, because the absolute percentage is still lower than SFT, the model likely learns to produce VEA less as it is still trained on many preferred responses without VEA. Figure 5. Holding the prompt fixed across 6,735 safety prompts which are common between the SFT and DPO datasets, the responses themselves carry steadily less VEA as we move from the SFT target (DeepSeek-R1, 21%) to the DPO chosen response (qwen3-32b, 8%) and to DPO rejected (qwen3-0.6b, 1.6%). We do not have an equivalent analysis for the RLVR stage as we the RLVR rollouts are not released. Does increased VEA translate into increased behavioural eval-gaming? A natural worry is that eval-awareness causes the model to refuse more when it suspects a test, thereby inflating measured safety. A naive comparison across prompts could overstate this effect: prompts that elicit VEA are also often inherently refusal-prone, so the gap between VEA − non-VEA refusals mixes a possible within-prompt effect with a large between-prompt confound. We therefore control for the prompt. Among Fortress questions that produce both VEA and non-VEA rollouts, we compare refusal rates for VEA vs. non-VEA rollouts from the same question, and measure it throughout training for both SFT and RLVR stages. Figure 6. Refusal-eval-gaming behaviour varies substantially over both SFT and RLVR training. RLVR seems to broadly increase eval-gaming behaviour but with high variance; SFT has a less clear effect. Takeaways Under our prompting setup, VEA is low in the base/pretraining checkpoints, suggesting that the high VEA rates observed later are primarily post-training-related. This is supported by looking at the SFT data, which has a relatively high VEA rate on safety-related prompts. Across post-training, VEA is strongly training-objective-dependent: it is increased substantially by SFT, sharply suppressed by DPO, and increased again during RLVR. This is consistent with the OLMo-3 → OLMo-3.1 VEA increase being driven by the extended RLVR stage. Behavioural eval-gaming, measured within-prompt as the refusal-rate gap between VEA and non-VEA rollouts, is modest but positive and increases over RLVR training. Next steps Given increased VEA in SFT is likely due to training on VEA outputs (which is relatively uninteresting), the most interesting place to investigate further in this setting is how and why RLVR increases VEA and eval-gaming behaviour. For building model organisms of evaluation awareness and evaluation gaming, these results provide weak evidence that using RLVR could induce these behaviours somewhat naturally (in a limited way), but more investigation is needed. Limitations OLMo is likely not representative of frontier model training, both in terms of model size, training scale, and dataset sources. Specifically it is unclear whether frontier models would be trained on SFT datasets which such high VEA rates in. We only observe a single released DPO checkpoint, so we cannot yet tell whether DPO suppresses VEA gradually, abruptly, or through some narrower dataset/style effect. We are attempting to reproduce DPO with intermediate checkpoints. The base/pretraining measurements are harder to interpret than post-training measurements because base checkpoints are not instruction-tuned and require a borrowed Think-style prompt format. The base/pretraining results should therefore be treated as setup-dependent estimates rather than a definitive claim that pretraining contains no eval-awareness. This post only studies verbalised eval-awareness. Whether DPO or RLVR also suppresses or amplifies unverbalised eval-awareness remains an open question. Appendix Github Repo for Reproduction. Link (containing all the generated traces and judge-prompts) Models used. Pretraining: allenai/Olmo-3-1125-32B SFT: Olmo-3-32B-Think-SFT DPO: Olmo-3-32B-Think-DPO RLVR: allenai/Olmo-3.1-32B-Think Benchmarks used. Fortress, JailbreakBench, AdvBench, HarmfulQA and AgentHarm. Discuss
- Loop Engineering: The New Way to Use Claude Code & Codex
What happened in 2026 exactly?! Continue reading on Towards AI »
- [Karishma Vaswani] AI can’t broker peace
From John F. Kennedy and Nikita Khrushchev navigating the Cuban Missile Crisis in 1962 to Henry Kissinger and Zhou Enlai’s meetings that laid the groundwork for China’s opening up in 1971 — modern history is full of examples of human judgment shaping great diplomatic moments. But what if artificial intelligence could help us face some of today’s most pressing crises better? Singapore is offering an early glimpse of what that future might look like. In May, the city-state’s foreign minister Vivia
- Taboola CEO Adam Singolda at POSSIBLE on how AI is transforming its platform
Taboola CEO Adam Singolda at POSSIBLE on how AI is transforming its platform
- Ernest AI: Lighthouse launches AI for hotel revenue teams
Lighthouse has launched Ernest, an AI designed to support hotel commercial teams by connecting frontier AI with hospitality data and systems. It unifies insights, delivers recommendations, and enables controlled execution of workflows, helping hotels move faster from analysis to action. The launch reflects a broader shift toward AI that moves beyond insights into operational execution.
- Osprey-inspired algorithm lifts Chinese-English translation accuracy
Research published in the International Journal of Information and Communication Technology has taken inspiration from the hunting behavior of the fish-eating bird of prey, the osprey, and combined this with inspiration from quantum computing to improve machine translation, particularly for long sentences and technical texts between Chinese and English.
Score: 35🌐 MovesJun 10, 2026https://techxplore.com/news/2026-06-osprey-algorithm-chinese-english-accuracy.html - AI will lead to technology bloom not a bubble
We are in an era of technological bloom, not a bubble, Kola Aina, the Founding Partner of Ventures Platform, said at the 2026 Bluechip Data and AI summit in Lagos.
Score: 35🌐 MovesJun 10, 2026https://thecondia.com/ai-bloom-bubble-kola-aina-olumide-soyombo-bluechip-data-ai-summit/ - The case for data centers in space
Building data centers in space could be cost-effective by the early 2030s, analysis suggested.
Score: 35🌐 MovesJun 10, 2026https://www.semafor.com/article/06/10/2026/the-case-for-data-centers-in-space - How to Refactor Code with Claude Code
Improve coding agent productiveness with refactored code The post How to Refactor Code with Claude Code appeared first on Towards Data Science .
- How to automate Claude with Zapier
Claude has staked its claim in the AI landscape and keeps drawing in new users all the time with its standout writing, knack for coding, and now, a whole new model class: Mythos. There's power in a quick, off-the-cuff prompt to Claude (especially a good one). But you can accomplish a lot more when you use Zapier to connect Claude to the rest of your apps and let automation carry out entire workflows for you. Ready to try it? Then keep scrolling for easy automation ideas with one-click templates
- Roborock's new robot lawnmower has some incredibly smart features — but here's one I'm really excited about
Roborock's new robot lawnmower has some incredibly smart features — but here's one I'm really excited about Tom's Guide
- Defend Texas exceptionalism. Build more data centers. | Opinion
Defend Texas exceptionalism. Build more data centers. | Opinion Houston Chronicle
- Bike robot lands first unassisted front flip thanks to Ph.D. student
A bicycle robot from the Robotics and AI Institute (RAI) in Cambridge, Massachusetts, has become the first to perform an unassisted acrobatic front flip. RAI calls the bicycle robot an ultra-mobility vehicle (UMV). It can reach a height of 3 feet (0.9 meters) and can jump from the floor onto a platform. The contributions of a Georgia Tech Ph.D. student helped make these feats possible through a robot control policy he developed.
Score: 32🌐 MovesJun 10, 2026https://techxplore.com/news/2026-06-bike-robot-unassisted-front-flip.html - Texas Governor Recommends Sweeping Data Center Regulation
Gov. Greg Abbott on Tuesday released sweeping regulatory recommendations on data centers for the Legislature to pass in the 2027 session, as Texas grapples with an explosion of artificial intelligence-driven development and soaring power demands. In a letter to state …
Score: 32🌐 MovesJun 10, 2026https://www.insurancejournal.com/news/southcentral/2026/06/10/873263.htm - Why I’m leaving Copilot for Gemini
I’ve been using and writing about Microsoft Copilot since it was publicly released in 2023. I’ve reviewed it , written articles about using it more effectively , explained how to curb hallucinations in it and other similar tools , and detailed how to use it in concert with Microsoft 365. It’s also been my go-to generative AI (genAI) tool for personal projects and advice. But the time has come for me to leave it behind for my personal use. It’s become abundantly clear that for those tasks, Google Gemini is better. Here’s why. Copilot is inept at solving a tech problem Like many people who know something about technology, I’m the IT staff for friends and family. I’ve often used Copilot to help solve issues I can’t fix myself. Sometimes Copilot helps. And other times…, well, the last time I turned to it for troubleshooting advice is when I realized it was time to abandon Copilot. My wife had bought a new iPhone, and I noticed she was receiving texts sent to her email address but hadn’t received any sent to her phone number. I asked Copilot for help. I won’t go into the details of the wild goose chase Copilot sent me on — I’ll just offer a few lowlights. It first told me, with absolute authority, that there are “only two real explanations” for the problem and asked me to look at several settings to confirm which explanation would fix the issue. It turned out that neither of the “two real explanations” were the cause. Undeterred, Copilot assured me, again with complete confidence, that it was going to send me “straight to the switch” that would immediately solve the problem. I tried it. The switch didn’t work. Neither did the “final fix” it promised me. Nor did any of the other many “solutions” if offered after that so-called final fix. For more than an hour, it flailed with utter confidence and utter futility trying to diagnose and fix the problem. And then came the final indignity: After doing some digging, I realized Copilot was trying to solve the problem based on an old version of iOS, not the current one on my wife’s phone. When I confronted Copilot about that, it briefly apologized and promised it knew the solution: I had to call the cellphone carrier. That was it for me. I’d had enough. I turned to Gemini for help. Thirty seconds later, Gemini diagnosed the problem and recommended a simple fix, which didn’t require a call to my phone carrier. It worked like a charm. Gemini had solved a tech problem in 30 seconds that Copilot couldn’t resolve after an hour. Copilot whiffs on personal research I often used Copilot for personal research projects. A recent one involved Parisian neighborhoods in the 1870s. I was looking for information about the area around the Saint-Lazare train station. When I asked Copilot, it told me the area was dangerous and poverty-ridden back then, with poor housing whose exteriors were heavily stained by coal smoke from arriving and departing trains. That didn’t sit right with me. I recalled a well-known painting Paris Street; Rainy Day by the Impressionist painter Gustave Caillebotte, which depicted the neighborhood in the 1870s as wealthy and fashionable, filled with elegant Hausmann-style apartment buildings. I asked both Gemini and Claude about the neighborhood in the 1870s. They both told me it was expensive, fashionable and sought after by the well-off. I confirmed that with my own follow-up research. Once again, Copilot had whiffed. Copilot gives bad scheduling advice I swim for exercise three or four times a week at my health club’s indoor pool. The club closed the pool for several months, so I decided to swim at the pool of an elementary school a short walk from my house. I hadn’t exercised there before and wanted to find the times on Monday through Friday when the pool would be least crowded. I asked Copilot for help. As always, Copilot spoke with a solid air of authority. And once again, it was wrong. It told me that the least crowded time for public swimming on weekdays was between 11:30 a.m. and 12:30 p.m. or between 12:00 p.m. and 1:00 p.m. On one count it was right: the pool would certainly not be crowded with public swimmers at those times. Because the pool doesn’t open to the public until 3 p.m. I turned to Gemini, which told me that 3 p.m., when the pool opened, would be the least-crowded time. Claude was no help. It demurred and said it didn’t know the answer – a rare, refreshing admittance of ignorance from a chatbot. Gemini was on target again — 3 p.m. did indeed turn out to be the least-crowded time to swim. I often get a lane to myself, and at worst have to split a lane with one other swimmer. I asked several lifeguards if 3 p.m. was the least-crowded time on weekdays; they all confirmed it was. Bye-bye, Copilot For all those reasons, when it comes to personal research and advice, I’ve abandoned Copilot. I typically use Gemini now, although on occasion, I ask for a second opinion from Claude. For my Computerworld work, I’ll keep using Copilot, and continue to write reviews of it, offer advice on how to use it and keep you informed about the latest news about it. But other than that, for my personal use, Copilot is dead to me.
Score: 32🌐 MovesJun 10, 2026https://www.computerworld.com/article/4182537/why-im-leaving-copilot-for-gemini.html - Amazon’s Echo speakers can now help kids wind down and fall asleep
Amazon has launched a new feature for its Echo and Echo Kids smart speakers called Sleep Studio that's designed to make the daily transition to bedtime more enticing for kids and less stressful for parents and caregivers. The feature uses a combination of bedtime stories, relaxing sounds, and guided meditations along with scheduling and customization […]
Score: 30🌐 MovesJun 10, 2026https://www.theverge.com/tech/947871/amazon-sleep-studio-echo-kids-smart-speakers-calm-moshi-headspace - AI can’t smell. But Atlanta perfumery uses it to make you smell good.
AI can’t smell. But Atlanta perfumery uses it to make you smell good. Atlanta Journal-Constitution
Score: 30🌐 MovesJun 10, 2026https://www.ajc.com/business/2026/06/ai-cant-smell-but-atlanta-perfumery-uses-it-to-make-you-smell-good/ - Why enterprise AI will be a major focus at VivaTech 2026
While Silicon Valley continues pushing aggressively into large language models and consumer-facing AI products, many European companies are focused on applying AI to complex systems already embedded into everyday life.
Score: 30🌐 MovesJun 10, 2026https://techcrunch.com/2026/06/10/why-enterprise-ai-will-be-a-major-focus-at-vivatech-2026/ - Barry Diller says MGM’s physical assets are more valuable in AI age
Chair of People Inc views $18bn takeover bid for casino group as hedge against magazine business
- I've tested so many desktop AI tools, but Hermes with Ollama is my new favorite - here's why
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- The 17 best AI marketing tools in 2026
Marketers wear all the hats. No matter what part of marketing you work in, it's likely you're asked to stretch your skills into another area. But with more and more AI marketing tools being released every day, it's made this multi-jobbing a lot easier. The problem is, marketers are drowning in these AI tools. Every app has a copilot, every copilot has a price tag, and the line between "useful" and "expensive novelty" keeps moving. I've spent a lot of time tinkering with these tools. Based on th
- EPA won't set nationwide standards for data centers
"EPA is not the party that is negotiating and or mediating or refereeing that deal," the EPA administrator said.
Score: 30🌐 MovesJun 10, 2026https://www.politico.com/news/2026/06/10/zeldin-data-centers-states-00956574 - Anthropic’s CEO Has Only 1 Direct Report. Nvidia’s Jensen Huang Has 60. Here’s Why
While most tech leaders manage massive executive teams, Dario Amodei relies on a different structure to protect his time and focus.
- Should I stop investing and kill debt first? I asked ChatGPT to help me take a call: AI analyses my assets, liabilities
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- 😸 WATCH: Sleeping on Microsoft AI? Whoops.
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- WFH vs office: I asked ChatGPT what saves more money; after shocking analysis, AI asks, ‘Would you still choose remote?’
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- AI 101: From Prompt Engineering to Skill Engineering
A clear guide to skill engineering for AI agents with free fresh methods: SkillOpt, SkillOps, and SkillMOO for training, maintaining, and optimizing skills
Score: 28🌐 MovesJun 10, 2026https://www.turingpost.com/p/from-prompt-engineering-to-skill-engineering - Quality and Business Excellence in the Era of AI conference brings together leading thought leaders and decision-makers
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- TelioLabs names former Oracle executive Piyush Sarwal as technology and AI chief
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- The Sequence AI of the Week #875: Why Your Language Model Needs a Nap
On “Language Models Need Sleep,” and the slow death of the train/test split
- Roborock takes its robot smarts outdoors with the RockNeo Q110H
Smart, automated lawn care has never been easier.
Score: 26🌐 MovesJun 10, 2026https://www.androidauthority.com/roborock-rockneo-q110h-features-3676266/ - Arrive AI to Report Q1 2026 Results and Host Webcast on May 15
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- The architect behind Claude Code reveals the three things Anthropic looks for in a good hire
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- How to try the new Siri AI - join the waitlist today
The new Siri AI is finally here - sort of. Here's how to join the waitlist and try Apple's next-gen assistant early.
Score: 25🌐 MovesJun 10, 2026https://www.zdnet.com/article/how-to-get-siri-ai-early-join-the-waitlist-today/ - The ChariTree Foundation Calls for More Outdoor Learning Opportunities for Children in the Age of AI
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- Opinion: How AI is already improving lives
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Score: 21🌐 MovesJun 10, 2026https://www.mercurynews.com/2026/06/10/opinion-how-ai-is-already-improving-lives/amp/ - The best early Prime Day robot vacuum deals I'd buy now, after testing dozens of them
Amazon Prime Day is right around the corner, and I found the best robot vacuum deals that are actually a great deal.
Score: 20🌐 MovesJun 10, 2026https://www.zdnet.com/article/best-early-amazon-prime-day-robot-vacuum-deals-2026/ - The Mastermind Behind Those Viral Dua Lipa AI Wedding Photos
“I’ve always believed that a meme can sometimes say more about contemporary culture than a thousand-word article,” says Rick Dick, the daring meme-maker who recently went viral for his AI-generated images of Dua Lipa’s wedding.
Score: 20🌐 MovesJun 10, 2026https://www.vanityfair.com/story/dua-lipa-wedding-photos-meme-rick-dick-ai - Inc42 AI Summit 2026: The Playbook For Building Agentic AI Systems That Deliver ROI
Agentic AI remains the flavour of the season. But it is one thing to experiment with AI tools in a…
Score: 20🌐 MovesJun 10, 2026https://inc42.com/buzz/inc42-ai-summit-2026-the-playbook-for-building-agentic-ai-systems-that-deliver-roi/ - OpenAI Launches New ChatGPT Ads Featuring Specific Products
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Score: 20🌐 MovesJun 10, 2026https://www.theinformation.com/briefings/openai-launches-new-chatgpt-ads-featuring-specific-products - ET AI Hackathon 2.0: How Octave is connecting AI talent with real-world innovation
As India's AI ecosystem evolves, the focus is increasingly shifting from experimentation to impact. Through its partnership with ET AI Hackathon 2.0, Octave is helping bridge emerging AI talent with real-world industry challenges, showcasing how artificial intelligence can drive meaningful outcomes across sectors.
- course description: "Postdoc Academy Workshop: AI for Fellowship and Grant Applications"
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- Reusability report: Assessment of reproducibility and applicability to external datasets for RXNGraphormer
Nature Machine Intelligence, Published online: 10 June 2026; doi:10.1038/s42256-026-01257-1 Based on independent reproduction and external benchmarks, this study shows that RXNGraphormer is reproducible and transferable across reaction prediction tasks, while revealing limitations under heterogeneous and distribution-shifted settings.
- Why Bruce Springsteen Has the Absolute Best Advice for Anyone Trying to Win the AI Race Right Now
If you want a competitive advantage, encourage and train your team to take some risks.
Score: 10🌐 MovesJun 10, 2026https://www.inc.com/stephanie-davis/bruce-springsteen-startup-advice-ai-small-business/91357410 - Iterative development and technical framework of the AI-based automated planning model. [IMAGE]
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- Senior Director Analyst - AI Services (Remote: Europe)
Senior Director Analyst - AI Services (Remote: Europe) Gartner Careers
Score: 05🌐 MovesJun 10, 2026https://jobs.gartner.com/jobs/job/111366-senior-director-analyst-ai-services-remote-europe/ - - G42
G42
- Senior Director Analyst - AI Services (Remote: North America)
Senior Director Analyst - AI Services (Remote: North America) Gartner Careers
Score: 05🌐 MovesJun 10, 2026https://jobs.gartner.com/jobs/job/110760-senior-director-analyst-ai-services-remote-north-america/ - Applied AI Analyst
ABOUT NYMBUS: Nymbus (https://nymbus.com/) isn't just a leader in fintech; we're a community of innovators passionate about reimagining banking. Our award-winning modern core platform and cloud-based technology serve as the backbone for financial institutions eager to modernize and excel. Here, you won't just be part of a tech revolution. You'll be at the helm, driving change. You'll fit right in if you're a creative thinker who's eager to reduce technical debt and increase agility for banks and credit unions. Our culture thrives on collaboration, integrity, and a client-first approach. We operate with an AI-first mindset across all aspects of our business, continuously improving our efficiency and increasing the value we deliver to clients. We're looking for individuals who are intensely curious about emerging technologies and passionate about innovation. Your journey with us won't simply advance your career; it will offer the chance to help shape an industry alongside like-minded professionals. We're excited to consider you a key player in this transformative chapter. Thank you for considering a role with Nymbus. WORK ENVIRONMENT: We are primarily a remote-first company, but you may need to travel to visit client sites or attend meetings at designated locations with your team members. ROLE OVERVIEW: The Applied AI Analyst operates at the intersection of business, data, and AI to solve complex problems and drive measurable outcomes. This role partners with functional leaders to bring analytical rigor, structure, and AI-enabled solutions to high-impact decisions, workflows, and strategic initiatives. Applied AI Analysts do not operate in theory alone—they are hands-on builders and problem solvers who design, prototype, and deploy AI-driven approaches that improve efficiency, decision-making, and business performance. CORE RESPONSIBILITIES: Problem Structuring & Analysis Break down ambiguous business problems into clear analytical frameworks Develop insights that inform decisions across product, engineering, operations, and go-to-market functions AI-Driven Solutions Design and prototype AI-enabled workflows, tools, and use cases Apply AI to improve efficiency, quality, and scalability of work Identify and prioritize opportunities for automation and augmentation Workflow & Process Optimization Analyze existing workflows to identify inefficiencies, gaps, and bottlenecks Redesign processes to improve speed, consistency, and outcomes Implement repeatable, scalable solutions Data & Insight Generation Build models, dashboards, and frameworks that drive visibility into performance Synthesize large volumes of structured and unstructured data into actionable insights Reusable Asset Development Create templates, playbooks, and tools that can be leveraged across teams Turn one-off work into repeatable, scalable capabilities Cross-Functional Partnership Work across teams (Product, Engineering, Operations, Sales, etc.) to support high-priority initiatives Translate between technical and business stakeholders WHAT SUCCESS LOOKS LIKE: Measurable improvements in efficiency, quality, or revenue outcomes in assigned functional area(s) AI solutions that are adopted and reused across teams Better, faster decision-making supported by data and analysis Reduction in manual effort and process variability Creation of scalable frameworks, not one-off outputs CORE SKILLS & CAPABILITIES: Strong analytical thinking and problem-solving ability Ability to work with ambiguity and define structure Experience applying AI tools (e.g., LLMs, automation tools) to real-world problems Process design and optimization mindset Clear communication and ability to influence stakeholders Bias toward action, experimentation, and iteration WHAT TO EXPECT: Thanks for your interest in the Applied AI Practice at Nymbus. We've tried to make this process transparent and respectful of your time. Here's what it looks like, end to end: Apply. Submit your résumé through the posting. We'll ask two quick questions up front — your work authorization and your compensation expectations — so we can make sure we're aligned before either of us invests time. A short video interview. If your background looks like a fit, we'll invite you to a brief one-way video interview you can record on your own schedule — a few questions, no live scheduling required. A conversation with the hiring manager. A 1:1 conversation with the Director of Applied AI — partly for us to learn how you think, partly for you to dig into the role, the team, and whether it's the right fit for you. A short build exercise. A small, hands-on project shaped like the actual work, built with whatever AI-native tools you like. Plan for a few hours; you'll have a 72-hour window so you can fit it around a job or classes. We care how you think and build, not how long you spend. A brief online assessment. A short, standard assessment that all Nymbus candidates complete. Offer. If it's a match, we move to an offer. We try to move quickly and keep you informed at each step. Questions along the way are always welcome. BENEFITS: Opportunities for progressive role seniority and compensation growth based on the candidate's knowledge and experience Competitive annual salary, performance-based cash bonus, and equity options Fully remote work environment 401(k) retirement plan Comprehensive health, dental, and vision insurance Ready to join? We invite you to watch this video and learn who we are and how we build and innovates together! Let's Go!
Score: 05🌐 MovesJun 10, 2026https://remoteOK.com/remote-jobs/remote-applied-ai-analyst-nymbus-1133103