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The U.S. economy grew a solid 2.2% in the second quarter. AP correspondent Donna Warder reports.

AP Washington correspondent Sagar Meghani reports inflation eased a bit in August as Americans spent more, though prices remain elevated.

     This week John and the Halogram finally take a small trip outside of Rushville, Nebraska, where it's  slightly chilly 36°F (2°C) with brief flurries to a barren dead world where it's a pleasant 22.2°C (72°F) with brief deluge of mind destroying hallucination causing terror rain. Still both prefer it over Nebraska. Join us, won't you?     Lanterns Minisode- A Lime in the Rain

I used to love cars with loud mufflers... now they disrupt me!! My dog was called overweight. If I'm a passenger in my wife's vehicle and we go to the gas station, should I pump her gas for her? All this and more on The Mark and Jess Replay!!

一碗地道的陕西油泼面，让自称“极度贪小便宜”的短视频博主孟阳被搭讪并“骗”进了 SO 的录音室。从澳门大学会计系、四大毕马威（KPMG），到雅思阅读老师、腾讯，再到辞职创业、搞脱口秀与短视频——自称对标沈腾和贾玲的“喜剧演员”孟阳，在网上一向以躺在床上讲八卦、穿着长沙澡堂买的 81 块钱搓澡巾睡衣示人。然而在镜头背后，她其实是一个极其典型、大脑时刻在高速进行“人际算法”与“预期管理”的 ISTJ。本期节目，SO 主播们与孟阳围坐在一起，展开了一场极高密度的 MBTI 认知碰撞。从爆款视频背后的“逐字稿 SOP”，到 ENTP（薇薇）的宏观碳基思考与 ISTJ（孟阳）具象微观算法的交锋；从面对极度 P 人朋友时的“修心”体验，到 T 人与 F 人面对情绪宣泄时的通透拆解。嘉宾与主播们深入探讨了：我们该如何看待自己的“镜像对角线”？合伙做生意与纯粹的友情该如何切割？以及，活出对角线最终是如何让我们“放过自己”的。【本期嘉宾 & 主播】本期嘉宾：孟阳（短视频博主、喜剧演员，全网社交媒体账户：@孟阳话很多）本期主播：薇薇、Coco、老柴【时间轴 & 精彩看点】01:26 缘起与搭讪：孟阳为了吃地道油泼面答应录播客，SO 主播们揭秘本期嘉宾邀请背后的故事。02:55 履历大公开：36岁金牛座、四大毕马威、雅思老师到腾讯，孟阳定义自己的喜剧演员之路。08:25 爆款背后的精密设计：分析“躺着拍视频”与 81 块钱搓澡巾睡衣的工作服心理学；孟阳拆解拒绝假装松弛的“逐字稿与回放 SOP”。12:14 脑内算法与宏观思考的交锋：孟阳自诩“AI 的人体化”与人际预期管理；薇薇分享 ENTP 脑内的碳基宏观思考与“活在当下”的冲突。18:56 T人与F人的情感与效率大乱斗：当 F 人失恋哭泣时，T 人在计算什么？孟阳与 SO 主播们对谈情绪安慰的 ROI 与陪伴的本质。23:04 I人与E人的社交微操：讨论“提前半小时到”的边缘试探，以及戴骨传导耳机过马路的内耗与心理建树。48:45 J人与P人的相处修行：“没订上酒店却在暴雨中大笑”的极致 P 人体验；主播们与孟阳拆解 J 人如何在失控行程中自我“修心”。01:11:18 友情与商业大辩论：要不要和朋友“过事过钱”？SO 主播们与孟阳对谈职场契约、合伙 18 个月定律与自营 IP 破局。01:43:06 荣格“个体化”与镜像对角线：薇薇引述荣格阴影人格与劣势功能；大家共同分享如何通过了解“对角线”最终实现“放过自己”。【本期提及的理论】心理学理论与著作概念：荣格（Carl Jung）“个体化”理论（Individuation）：探讨阴影人格（Shadow）与劣势功能（Inferior Function）的整合与反噬。MBTI 人格类型指标：深度碰撞了 ISTJ、ENTP、INTJ、INFJ、ENFP 等类型在认知维度上的对角线特征。【互动与关注】你有遇到过和你完全处在“镜像对角线”的朋友或伴侣吗？在与他们相处时，你曾经被他们深深吸引，还是曾感到抓狂？你又是在哪一个时刻开始学会“放过自己”的？欢迎在评论区留言分享你的故事与想法！欢迎在各大播客平台搜索订阅 《Slightly Open》欢迎关注本期嘉宾社交媒体账号：@孟阳话很多（全网同名）

Ps Jason Schroeder--27 September 2026

St. Paul Mayor Kaohly Her declined comment today when asked about the lawsuit filed against her by Police Chief Axel Henry.And gas and diesel prices in Minnesota have been flat or slightly falling over the past couple days.Those stories and more in today's evening update from MPR News. Hosted by Emily Reese. Music by Gary Meister.

Lana revealed that back in the 1860s, local time was so different across the country that Christchurch was 8 minutes ahead of Wellington. Things got so confusing for telegraphs that everyone had to sync their clocks to one Wellington sundial at noon. Keen for more? Follow The Breakfast Club on socials and give us a 5-star rating on Spotify or Apple.

Lakeview is apparently trying to fit an entire year of ministry milestones into a couple of weeks. Last Sunday they sent out Cross & Crown Church, and this Sunday they broke ground on a brand-new children's building. Tim is riding a pretty incredible wave of momentum, although at this point the wave may be mostly caffeine and exhaustion.Meanwhile, Andrew has somehow become the official prayer guy for the City of Safety Harbor. If there's a microphone, a city official, and something happening downtown, apparently somebody has Andrew's number.Tim is also discovering one of the realities of preaching verse by verse through Exodus: eventually you have to preach the parts nobody puts on a coffee mug. We're talking about what to do when the next passage is important, inspired, and, if we're being fair, a little boring.And a few weeks after Safety Harbor paid off its mortgage, Andrew finally gets to tell the story of the actual mortgage burning, complete with flash paper and his brief career as an amateur magician. Because apparently simply saying, “We paid off the building” wasn't dramatic enough.It's groundbreaking, church planting, civic prayers, difficult texts, and lighting things on fire in church. Just another couple of weeks in ministry.

US President Trump said he thinks a settlement will be reached with Iran, and noted that some US-Iran communication has taken place even today and that the relationship with Iran is developing.US President Trump said envoys Witkoff and Kushner met with Iran mediators and that there were lots of good thoughts. Trump later commented that he thinks Iran will do something good.Trump also warned that they may have to blow up Pickaxe Mountain and that they will attack if they see activity. He reiterated that he thinks the Iran war could end after the Midterms, maybe before.Crude futures were subdued overnight after declining yesterday amid hopes for diplomacy following US-Iran talks on the sidelines of the UN General Assembly.APAC stocks were ultimately mixed following the similar handover from the US; European equity futures indicate a positive cash market open.Looking ahead, highlights include Global S&P PMIs Flash (Sep) and the SARB Policy Announcement. UN Meetings include the Trump-Sharif meeting, Senior Iranian leaders-GCC meeting, and Iranian President's address. Speakers include ECB's Vujcic, Cipollone & Lane, Fed's Barr. Supply from Germany and the US.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Si shared some facts about salmon swimming up to 3,000 kilometres back to the exact spot they were hatched, but Lana wasn't convinced and voted him down. Amanda the teacher gave Si a yes, but Bondy and Lana kept him from getting a clean sweep. Had a laugh? Follow The Breakfast Club on Facebook and Instagram, and chuck us a 5-star rating on Spotify or Apple.

Sorry about the 2 week layoff after we thought we'd come back, but it was work commitments this time that held things up. But we finally made it! Quite a lot has happened in the last couple of weeks, so we've tried to compress it all into a 90 minute digest with minimal tangents (only said minimal, not none!)There was the Italian Grand Prix with an incredible last(ish) to first drive from this year's champpion elect, Kimi Antonelli, then the utter pointlessness of the parade around the newly build MadRing on the outskirts of Madrid's city centre, and what an utter thrill-a-week that "race was"There's bits of news too, including the latest attempt by MBS to dictate not only how F1 is run, but who's driving the cars, and a load more waffle too, At least there's Baku to look forward to this weekend, and don't forget everything is a day earlier than usual with the race on Saturday and don't forget to see how you did in the ⁠Prediction League⁠ and ⁠Official F1 Fantasy League⁠ too.And if you want to join in with the chat during the races (along with testing, practice and qualifying) head over to our ⁠⁠Discord⁠⁠ where there's always a great crowd of people to watch along with. And on the socials we've got our ⁠⁠Facebook⁠⁠, ⁠⁠Instagram⁠⁠, ⁠⁠BlueSky⁠⁠ & ⁠⁠Twitter (or is it X)⁠⁠ and Paul's attempts at Sim Racing on our ⁠⁠Twitch⁠⁠ channel. And if you want to support us you can donate to our ⁠⁠Patreon⁠⁠ as well from as little as £/$/€ 1 per monthEnjoy 

US President Trump said he had meetings regarding Iran and that Iran is not doing well; the Pakistani Interior Minister held consultations in Iran on efforts to advance the peace process.US Treasury Secretary Bessent said he will present US President Trump with a US-China AI pact, while Trump is to decide on a US-China AI deal this week.APAC stocks mostly gained following the advances on Wall Street, where the Nasdaq outperformed.DXY was little changed but held on to the prior day's gains; 10yr UST futures took a breather following recent gains.European equity futures indicate a slightly positive cash market open, with Euro Stoxx 50 futures up 0.2%.Looking ahead, highlights include US ADP Employment Change Weekly, Italian 2025 Deficit Update, NBH Policy Announcement. UN Meetings: UN General Debate including Trump, Macron, Burnham; Trump-Zelensky meeting; Trump-Burnham meeting; Trump-Gulf Leaders meeting. Comments from ECB's Lagarde & Nagel, Fed's Williams, Jefferson & Barkin. Supply from UK, Germany and the US.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

FFoDpod.com   Patreon   Merchandise   CC-BY-SA  "SCP-761" by HK-016, rewritten by Anonymous, from the SCP Wiki. Source: https://scpwiki.com/scp-761. Licensed under CC BY-SA.

Slightly foggy morning / AC/DC last night / Nice weather weekend / What's with all the bees on the golf course?? (1:40); Winnipeg Jets coach Scott Arniel on the move to permanent Daylight Saving Time (9:35); Unusual pests, like the bees all over our golf balls at Maplewood yesterday and Lorette last week (17:45); Greenland..WHAT'S THE DEAL? Europe preparing for Russian aggression? (26:10); Fuel shortages and diesel costs rising sharply (33:50); 2026 CAA EV Circuit: EVs go the distance, but charging, efficiency and costs vary (43:10); SPORTS! - Bob Irving (50:40); Why are there so many bees on the golf courses?? And why are they obsessed with the golf balls?? (1:03:05); New book: PLAYING THE CHANGES... A Musician's Memoir - Colin James (1:11:20).

Keith Peiris, Co-Founder and CEO of Lightfield, joins Sam Jacobs, AJ Bruno, and Asad Zaman to talk about building an agentic CRM that launched in November 2025, has been adopted by more than 5,000 customers, and just raised a $47 million Series A led by Andreessen Horowitz. Topics include why every system of record was really a forecasting tool, how to get reliable output from non-deterministic models, and the two reasons companies actually rip out Salesforce or HubSpot. Plus, a spirited debate on whether the SaaS partner ecosystem is dead, why AI-native deal sizes at 200-person companies can rival enterprise contracts, and how Lightfield landed on a mix of seat-based and consumption pricing.   Key Takeaways:   - Plan for models that will always make some mistakes. Keith builds Lightfield on the assumption that models stay non-deterministic, and calls reliability "a harness problem" that they're making fantastic progress against. - Use LLMs for insight and plain code for arithmetic. When Asad asked whether auditing AI forecasts saves anyone any time, Keith described a one-time implementation where Lightfield's head of finance checked every formula: "We converted some things from prompts to real code, right? I don't want LLMs doing math. I want real code doing math." That got the dashboards 95% of the way there, leaving the models to flag which deals were on the cusp because of missing features or competition. - Slightly better AI output won't justify replacing a CRM. After several rip-and-replace projects off Salesforce and HubSpot, Keith said marginally better agentic emails are "not a good enough reason to change a system of record." The two reasons that do move customers are believing one model of the business improves every stage of the revenue funnel, with SDRs pulling real-time case studies from customer success, and higher-level scenario planning, where "the modeling really matters. The data completion really matters." - Put predictable work in the seat price and meter the work with visible ROI. Lightfield tried both extremes, and pure consumption backfired: "It's like going to a store where everything's really expensive, that our customers didn't touch anything." The seat and platform fee now covers the core CRM, call recording, and the data model, while pipeline generation, AI workflow automations, and scenario planning run on consumption, because "most revenue leaders, CEOs… have an uncapped budget for business intelligence."   Connect with the Hosts & Guests:   Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/ Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at STA - https://www.linkedin.com/in/azaman1/ Guest: Keith Peiris, Co-Founder & CEO at Lightfield - https://www.linkedin.com/in/keithpeiris/   Topline is more than a Podcast:   Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Check us out on YouTube for the #1 video podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack

Si shared the heartbreaking back story behind one of the world's most famous Christian hymns, written by Horatio Spafford after losing his four daughters at sea. Safe to say it got a big tick from the rest of the team. Had a laugh? Follow The Breakfast Club on Facebook and Instagram, and chuck us a 5-star rating on Spotify or Apple.

Ps Emma Schroeder

Only favorite tunes specially selected for unexpected event by Abyssal promo group ❤️ Endless thanks for inviting me, otherwise it would never happen ? Slightly edited for comfortable listening ? 01. Calibre - No One Gets You (Original Mix) [Signature] 02. M-Church - Morning Coffee (Original Mix) [Smooth N Groove Records] 03. Calibre - Space Time feat. Cleveland Watkiss [ID] 04. Clusion - Blue Shade (Original Mix) [FIVE ALLEY] 05. Furney - Madison (Original Mix) [Soul Deep Exclusives] 06. Adibanti - Self (Original Mix) [Drum&BassArena] 07. 747 - Pacific Spirit [ID] 08. Lunanescence - Overcast (Original Mix) [Omni Music (UK)] 09. Furney - Compo (Original Mix) [Soul Deep Exclusives] 10. Lenzman, Riya - Open Page (Original Mix) [Metalheadz] 11. Wez Walker - Smile (Original Mix) [Influenza Media] 12. Bronski - Serious Bizniz (Original Mix) [Soul Deep Digital] 13. Simplification & Translate - Something Good (Original Mix) [Soul Deep Exclusives] 14. R1C0 - Follow Me Down (Original Mix) [Soul Deep Digital] 15. Chug - Take It Easy (Original Mix)[Smooth N Groove Records] 16. M-Church - Lost In Pads (Original Mix) [Soul Deep Exclusives] 17. M-Church - Birds of Summer (Original Mix) [Soul Deep Exclusives] 18. Shiny Radio, La Kos - Turn Me On (Original Mix) [Soul Deep Digital] 19. Anushka - Never Can Decide (Ivy Lab Remix) [Brownswood Recordings]

Welcome to The Turf Zone podcast. This episode features the article “Managing Poa annua on Ultradwarf Bermudagrass Greens” written by Chris Neff, Senior Agronomist, East Region and Jim Brosnan, Ph.D., Professor, University of Tennessee. Poa annua is one of the most persistent and disruptive weeds in ultradwarf bermudagrass greens. In this article, we explain why Poa annua thrives, how it impacts putting green playability and performance, and the cultural, chemical and routine maintenance practices that can help keep it under control. Success depends not on a single herbicide, but on an integrated management program that promotes healthy bermudagrass year-round. Key Takeaways: Poa annua is problematic in ultradwarf bermudagrass putting greens because the plant characteristics and timing of active growth for the two grasses are so different. Fully eradicating Poa annua from ultradwarf greens is very challenging, so the goal for most courses is to minimize its presence and impact on playability. Pre and postemergence herbicides are part of successful control, but resistance is an issue and herbicides alone cannot solve Poa annua problems. Cultural practices and growing environments that favor dense and healthy ultradwarf greens are a critical part of any Poa annua control strategy. Poa annua remains one of the most persistent and disruptive weeds in ultradwarf bermudagrass putting greens, particularly throughout the Southeast. From a consulting perspective, it is less a question of whether Poa annua will be an issue and more a matter of how well it is managed within the system. Its biological advantages, rapid germination, prolific seed production and adaptability to low mowing heights make it uniquely suited to exploit weaknesses in bermudagrass stands, especially during seasonal transitions. The primary issue Poa annua presents is inconsistency. Bermudagrass and Poa annua differ significantly in growth habit, color, leaf texture and seasonal performance. These differences manifest most noticeably on the putting surface, where uniformity is paramount. Poa annua tends to produce a lighter green color and a more upright growth habit, creating a mottled appearance that is often unacceptable to golfers. The differences in growth habit and timing of optimal growth also lead to bumpy surfaces, especially when Poa annua is growing actively in shoulder seasons while bermudagrass is growing slowly or is fully dormant. Early spring can be especially problematic in the Southeast because conditions are ideal for Poa annua while bermudagrass is often worn down by winter play. Aggressive Poa annua seedhead production, particularly in late winter and spring, adds to the variability in ball roll. Even under intensive management, seedheads can disrupt smoothness and trueness, leading to dissatisfaction among golfers. Beyond aesthetics and playability, Poa annua also introduces agronomic challenges. It is inherently less heat and drought tolerant than bermudagrass. As temperatures rise in late spring and early summer, Poa annua often declines rapidly, often leaving voids in the putting surface. This decline is typically accelerated by disease pressure during hot, wet and humid conditions, as stressed Poa annua becomes highly susceptible to pathogens. The resulting voids reduce surface uniformity and create opportunities for further weed encroachment. Another agronomic issue is that when Poa annua is actively growing during cooler weather, ultradwarf bermudagrass greens are growing slowly, if at all. Mowing and/or rolling can help minimize the impact of Poa annua plants on ball roll, but these practices can also increase wear on the desired bermudagrass, which can lead to more opportunities for Poa annua to gain ground. As the population of Poa annua in the putting greens increases, these and other agronomic issues become more challenging to manage, so keeping Poa annua under control as much as possible is the primary goal. Fertility Program Achieving this level of surface dominance begins with a carefully balanced fertility program designed to sustain consistent growth without encouraging excessive thatch accumulation or unwanted vertical elongation. Nitrogen applications should be delivered in light, frequent increments to promote steady lateral spread and recovery, rather than flushes of growth that can lead to scalping or increased disease susceptibility. Equally important, potassium should be maintained at adequate levels to enhance plant resilience, particularly in the face of summer stressors such as heat, traffic, and periodic drought. Elevated soil phosphorus levels have been associated with increased Poa annua populations in closely mown turfgrass environments (Guertal & McElroy, 2018). Excessive phosphorus can provide a competitive advantage to Poa annua, allowing it to establish and persist more readily in putting greens and other intensively managed turf areas. For this reason, phosphorus applications should be guided by soil test results and targeted to maintain levels within agronomically appropriate ranges, thereby promoting the competitiveness of the desired bermudagrass while reducing conditions that may favor Poa annua encroachment. Rutgers University turfgrass research has noted that elevated soil pH can be associated with increased Poa annua pressure in creeping bentgrass, making pH management an important cultural tool in suppression programs (McNally et al., 2024). Maintaining soil pH in a slightly more acidic range can help strengthen bermudagrass competitiveness and reduce Poa annua populations over time. Moisture Management Moisture management is equally critical in tipping the competitive balance toward bermudagrass during the summer months. Excessively wet conditions, whether from overwatering, poor drainage or prolonged rainfall, create an environment highly conducive to Poa annua germination, survival, and eventual reestablishment. In contrast, firm, well-drained surfaces inherently favor bermudagrass by promoting deeper rooting, improved oxygen exchange, and greater heat and traffic stress tolerance. Superintendents should target a consistently moderate soil moisture profile, carefully balancing the need to prevent drought-induced stress with the equally important goal of avoiding prolonged saturation. This requires a disciplined, responsive irrigation approach, in which water is applied based on plant demand and environmental conditions rather than on fixed schedules. Allowing the surface to dry down between irrigation events not only enhances firmness and playability but also puts Poa annua at a competitive disadvantage, encouraging bermudagrass to expand laterally and occupy available space. Using portable moisture meters to guide putting green irrigation can significantly improve the precision and effectiveness of this program. By providing real-time, site-specific data, these tools allow superintendents to make informed irrigation decisions that optimize water-use efficiency and maintain optimal rootzone moisture. Improving surface and subsurface drainage is also an effective part of Poa annua control. Areas of putting greens that tend to gather and hold more water are often where a high amount of Poa annua encroachment is observed during cooler and wetter periods, so any projects that can be done to improve drainage will enhance bermudagrass health and limit the amount of moisture available for Poa annua. Cultural Practices Cultural practices during the summer should be intentionally structured to enhance the density and vigor of bermudagrass and actively disrupt the life cycle of Poa annua. Topdressing and aeration are fundamental practices for maintaining healthy ultradwarf greens, and they place Poa annua at a relative disadvantage by limiting the amount of moisture that is held in the upper portion of the putting green rootzone. Incorporating vertical mowing and grooming into the program refines surface quality and triggers new shoot growth for improved density and increased surface smoothness. The frequency, intensity and timing of all cultural practices should be carefully managed to maintain bermudagrass vigor and allow adequate recovery time. Excessive stress weakens the competitive advantage of bermudagrass, creating favorable conditions for Poa annua germination, establishment and long-term persistence. Slightly raising the mowing height on ultradwarf putting greens heading into fall can help improve Poa annua control by reducing mechanical stress on the turf. Healthier, less-stressed bermudagrass develops greater density and is better able to compete with Poa annua. The additional leaf tissue will also help maintain bermudagrass density and coverage through the winter, when wear can create voids for Poa annua to occupy going into spring. Minimizing shade on ultradwarf putting greens is another important “cultural practice” that will help minimize Poa annua issues. Bermudagrass requires high light levels to maintain density and vigor, whereas Poa annua can persist under lower light conditions. Trees or surrounding vegetation that intercept sunlight will weaken bermudagrass and create favorable conditions for Poa annua establishment. Increasing sunlight through selective tree pruning or removal can strengthen bermudagrass competitiveness and should be considered an important component of any long-term Poa annua management program. Herbicides While routine maintenance practices that support bermudagrass health form the foundation of Poa annua control, a well-structured herbicide program is essential for long-term success. Preemergence herbicides are the cornerstone of this approach. Applications should be timed to coincide with declining soil temperatures in late summer and early fall, typically when temperatures at the 2-inch depth consistently fall below 70 degrees Fahrenheit. Poa annua emergence typically occurs when 24-hour mean soil temperatures are sustained at or below 66 degrees Fahrenheit (Taylor et al., 2021). Application timing is critical: Treatments made too early may break down before peak germination, while those made too late will fail to provide effective control. Sequential applications will be required to sustain an effective chemical barrier throughout the shoulder seasons when Poa annua can most easily invade ultradwarf putting greens. Once soil temperatures are below 66 degrees Fahrenheit, applications should deliver both pre and postemergence control from varying mode-of-action groups. Advanced management programs frequently incorporate tank-mixing and rotational sequencing of multiple herbicide modes of action across the preemergence and postemergence spectrum. For example, the preemergence herbicide methiozolin (PoaCure) can be alternated with pronamide (Kerb) to balance soil residual activity, while herbicides such as amicarbazone (Xonerate) and flazasulfuron (Katana) can offer postemergence activity. Some turfgrass managers have expressed curiosity about herbicides that may be effective for controlling Poa annua but are not labeled for use on putting greens. In fact, some of these products have been studied for Poa annua control on ultradwarf greens in research settings for this reason. It is important to remember that research putting greens are not subjected to many of the stressors commonly found on golf courses, primarily traffic and shade. Additionally, the herbicide label is the law when it comes to any application. Many courses throughout the Southeast are plagued with herbicide-resistant Poa annua on putting greens, particularly populations that are not controlled with Group 2 herbicides such as foramsulfuron (Revolver). From a resistance management standpoint, rotating herbicides across distinct sites of action is not optional, it is essential. Repeated reliance on a single mode of action, particularly within ALS-inhibiting chemistries, has contributed to documented cases of resistance in Poa annua populations. A diversified, programmatic approach that integrates multiple chemistries not only improves short-term control but also preserves long-term herbicide efficacy. To expand the modes of action available for Poa annua control, some superintendents have considered or applied nonselective herbicides to dormant ultradwarf putting greens where this use is not explicitly prohibited by the product label. This type of application carries substantial risk, it is not recommended by the authors of this article, and we do not believe it should be considered a viable management option. Although bermudagrass may appear fully dormant, herbicides can still translocate to the crowns and roots, increasing the risk of turf injury, delayed spring greenup and reduced recovery. Any use of nonselective herbicides should be evaluated carefully based on product label requirements, turfgrass condition, environmental conditions, and the potential impact on spring transition. Successful implementation of an herbicide program to control Poa annua requires precise attention to turfgrass tolerance, environmental conditions and product label guidance and restrictions. Ultradwarf bermudagrass exhibits variable sensitivity to many postemergence chemistries, particularly when it is under stress or during spring transition. Applications made during periods of fluctuating temperatures, low carbohydrate reserves, or reduced growth can result in chlorosis, thinning or delayed recovery from dormancy. As such, rate selection, application interval and environmental timing must be carefully calibrated to balance effective Poa annua suppression with acceptable levels of bermudagrass safety. Managing the Presence of Poa annua Despite best efforts, many facilities will continue to contend with significant Poa annua populations in their ultradwarf greens, particularly during the fall, winter and early spring. In these situations, the focus must shift from eradication to minimizing the impact on playability. This requires a thoughtful, integrated approach that addresses both agronomic considerations and golfer expectations. During the cooler months, Poa annua will become more pronounced on putting surfaces. Mowing and rolling are essential for controlling its vertical growth and maintaining smoothness, but there must be a balance between managing the impact of Poa annua on playability and wearing down the desired bermudagrass that is growing slowly or not at all. Rolling can help mitigate the impact of seedheads and improve ball roll consistency without placing as much additional stress on the bermudagrass as mowing would, but excessive rolling during periods of slow bermudagrass growth can lead to turf thinning, particularly around putting green edges, which can lead to more Poa annua becoming established. When Poa annua populations are still scattered and individual plants are small, hand removal can be one of the most practical and precise tools available. While this process is labor intensive, physically removing isolated plants before they expand or produce seed can help protect surface uniformity without potentially weakening the bermudagrass base with herbicide applications. The key is timing. Plants should be removed by the roots while they are small enough to leave only minimal disruption to the surface, followed by careful repair so the surrounding bermudagrass can quickly knit back into the void. Bermudagrass is uniquely adapted to tolerate and respond favorably to frequent, low mowing during periods of active growth when supported by adequate fertility and moisture. Under these conditions, the turf exhibits improved density, finer texture and greater resiliency to traffic and environmental stress when mowed lower. On the other hand, Poa annua is less tolerant of sustained low mowing heights as temperatures rise and environmental pressures increase. As bermudagrass growing conditions improve, frequent low mowing further weakens Poa annua plants and reduces seedhead production. Superintendents should capitalize on this opportunity in late spring and early summer by implementing mowing practices that promote bermudagrass performance without overextending the plant. This includes maintaining sharp, well-adjusted equipment to ensure clean cuts and avoiding abrupt mowing height changes that could induce stress. Transition Back to Bermudagrass As temperatures begin to rise in spring, the transition back to bermudagrass dominance presents both challenges and opportunities. This is often the most volatile period for putting green performance, as declining Poa annua and emerging bermudagrass compete for space. Patience and restraint are essential during this time. Aggressive practices aimed at removing Poa annua too quickly can lead to unacceptable surface disruption, while insufficient management may delay bermudagrass recovery. Gradual adjustments in cultural practices such as verticutting and topdressing, can facilitate a smoother transition. Increasing nitrogen inputs to favor bermudagrass, combined with strategic aeration and topdressing, can accelerate recovery from winter and encourage the bermudagrass to fill voids left by declining Poa annua. Continued use of PGRs may be warranted to suppress any remaining Poa annua populations and maintain surface consistency. Conclusion Communicating with stakeholders is also a key component of successful Poa annua management. Golfers must understand the challenges of maintaining ultradwarf bermudagrass putting greens in regions with high Poa annua pressure. Setting realistic expectations and providing clear explanations of management strategies can go a long way in fostering support and reducing frustration during transitional periods. Ultimately, managing Poa annua in ultradwarf putting greens is a year-round endeavor that requires a proactive, integrated approach. There is no single solution, and success is rarely defined by complete eradication. Instead, the goal is to minimize impact on playability and aesthetics while promoting the long-term health and dominance of bermudagrass. Superintendents who embrace this philosophy by combining sound agronomic practices with strategic chemical inputs and effective communication will be best positioned to navigate the challenges Poa annua presents. While it may never be fully eliminated, the influence of Poa annua can be managed to a level that allows for consistently high-quality putting surfaces, even under the most demanding conditions. You have been listening to The Turf Zone Podcast. Follow The Turf Zone on X, Facebook and LinkedIn for all things turfgrass, featuring podcasts, magazines, events and more. For a complete list of references as well as supplementary photos, read this article in the August / September 2026 issue of Tennessee Turfgrass magazine on www.TheTurfZone.com The post Managing Poa annua on Ultradwarf Bermudagrass Greens appeared first on The Turf Zone.

Chris and Jack are here to entertain you over your lunch break in the 40k Lunchtime Show! #warhammer40k #warhammer #competitive #podcast #6++ #6plusplus #uktc #chapterapproved https://www.gofundme.com/f/that-6-plus-plus-fundraiser Join our Discord: https://discord.gg/JcFMpcYvkV Join this channel to get access to perks: https://www.youtube.com/channel/UCYwPTgmHC5VWu4Xn-qMg31g/join All our Links in one handy place: https://linktr.ee/6plusplusgaming

When it comes time to sack the tradie; What is your "Slightly illegal" raqcket; Listen to yesterday's Phintervention; One of Brisbane's suburbs makes an international list!See omnystudio.com/listener for privacy information.

Lana found out that New Zealand actually has the most knobs per capita in the world. Turns out early settlers named heaps of small hills "knobs", including Bald Knob, Nervous Knob and even Devils Knob in Nelson. If we made your morning a little better, follow The Breakfast Club on Facebook and Instagram and leave us a 5-star review.

The co-presenter wheel keeps on spinning and today, it's landed on Fi and Young Eve. She's back from her travels. They cover faecal waterslides, gangster grannies, Tyler the Creative, and the overwhelming nature of audio description. Plus, best-selling novelist William Boyd discusses his latest book 'Cold Sunset'.You can buy tickets to the Cheltenham Literature Festival here: www.cheltenhamfestivals.org/events/jane-garvey-and-fi-glover-with-anneka-riceYou can check out our YouTube channel here: https://www.youtube.com/@OffAirWithJaneAndFiOur most asked about book is called 'The Later Years' by Peter Thornton.If you want to contact the show to ask a question and get involved in the conversation then please email us: janeandfi@times.radioFollow us on Instagram! @janeandfiPodcast Producers: Eve SalusburyExecutive Producer: Rosie Cutler Hosted on Acast. See acast.com/privacy for more information.

We're back (kind of), and Fat Joe picked a wild week to call himself the Blackest man in the room.  Ryan needed a summer to touch grass. Joyhdae turned 43 and drank accordingly. In between, we recorded on the 25th anniversary of 9/11, watched Apple ask $2,000 for a phone that folds, and got very into our feelings about who gets to say what and who gets called what. This is the refresh, again. Fewer hot takes for the sake of hot takes, more of the actual conversations we have in our group chat.⸻Segment BreakdownWe're Back (Barely)Ryan's summer break, Joyhdae's 43rd birthday hangover, and why the show is getting a little more grown from here on out.9/11, Twenty Five Years LaterA real moment before the chaos. Rest in peace to the people we lost, and to the New Yorkers who were never quite the same after.Am I The Asshole: Baby Number TwoA brother has quietly covered his sisters kids school fees for two years. Then his sister announced baby number two, and he decided he's done contributing. Half the family calls it self-preservation. The other half calls him the villain of the year.Fat Joe Says He's “The Blackest”Fat Joe went on the Joe Budden Podcast and declared himself the Blackest N-word in the room, in front of a room full of Black men who said nothing. We break down race versus ethnicity versus nationality, and the Beyoncé double standard nobody wants to name.iPhone Duo, Apple's $2,000 AskApple unveiled its first foldable iPhone and Ryan is ready to  order one. Joyhdae is staying exactly where she is, thank you.Wordle, The Ancestors, and Aging GracefullyA new PCP, an unfortunate Hims ad, and a Wordle winning streak Ryan insists is spiritual and Joyhdae insists is theft.Dad vs. Auntie JokesTwo bottles of Veuve deep. Slightly unhinged, extremely unfiltered, exactly what you came for.⸻ Is Fat Joe Black? Is Beyoncé wrong for singing in Spanish? Would you cut off your nephew's tuition? Tell us in the comments. We read every single one.If you're new here, welcome to the Framily. Subscribe, hit the bell, and share this with the group chat that's currently arguing about the same thing.⸻Connect With Us:Email: Virgoseasonshow@gmail.comWebsite: Virgoseasonshow.comYouTube, TikTok, Instagram, & Threads: @VirgoSeasonShowRyan: @OhBlackRyanJoyhdae: @JoyhdaeSubscribe, leave a review, & hit the bell to turn on notifications.  ⸻We're grateful for your continued support.  We couldn't do it without you.  This show is a labor of love.  We thank you!⸻CHAPTERS 00:00 — Intro00:05 — We're Back!09:12 — Remembering 9/1112:20 — Hair Loss And 80s Nostalgia16:33 — Meditation And Wordle Wars21:47 — Am I The Asshole?38:52 — Techtember Apple Hype43:39 — Ryan Texting Chaos49:08 — Fat Joe N Word Controversy01:06:42 — Dad vs. Auntie Jokes01:11:25 — Outro

“我们穷极一生，其实都是在做这件事情。”这一期我们居然请来了知名的天体物理学家张双南老师，并从一个刁难问题开始：请双南老师介绍自己，但不提社会身份。研究黑洞、造望远镜、把探测器送上天，他答得很快——我没有社会身份。于是一场关于宇宙的对话，从一开始就落向地球，降落人间。双南老师有一个“pet”，是一颗他发现的黑洞，它一有动静他就格外关心。他说父亲临走前对母亲说的是“咱们家这么好，我真的不舍得走”。他来SO做客，真正想聊的，不是自重大的科研发现，而是他最近开始追问的一个问题：人活着，到底有什么意义？他向两位顶级哲学家讨过答案，一个说意义就是追问意义，一个说意义是贡献，他用做科研的方式去找反例，然后把它们一一证伪。最后给出的是一个动词——联结。不是“关系”那种继承来的静态事实，也不是英文里那个物理性的 connection，而是我们主动伸出手去建立的那部分。几个小伙伴被追问“此刻你觉得人生的意义是什么”，同一个问题，几个人给出了不同方向的轨迹——有人从“爱情大过天”走向“关于我爱且爱我的人”；有人从宏大的使命，一路走回自己、走向联结；有人的人生意义从来都是从关系出发。那么，究竟是我们赋予关系以意义，还是关系先一步定义了人为什么活着？AI 时代，人活着的意义是否被重新定义？如果人的意义建立在能力之上，那么当能力可以被替代，意义也就随之被消解。但还有一种反向的读法：过去人是把自己当机器在用，现在那部分被拿走了，人反而更有机会像人。工具性被剥离之后，剩下的那个东西——好奇、独特性、主动伸出去联结的手——才是人本来就该有的样子。如此浩瀚的宇宙里，一个小小的人为什么依然探究意义。听完这一期，答案可能并不新鲜，只是终于变得清晰：我们最后留下来的，是和这个世界建立过的那些联结。时间轴01:00｜开场：脱下社会身份，一个人被要求不带头衔地自我介绍时，最先浮出来的往往不是“我是谁”，而是我们有多依赖那些标签来完成自我陈述。双南老师说“我没有社会身份”，但接着讲了造望远镜、黑洞、中子星——那些也是身份吗？也许真正的问题是：当所有可以写进简历的部分都被拿走，我们想与他人产生联结，该如何介绍自己吗？08:05｜不敢向双南老师开口那一次：想邀请他来做客SO，却先替对方想好了拒绝的理由，于是问题从未被问出口。真正难的不是被拒绝，更多来自我们对“合适不合适”的提前判决和自我设限。23:18｜大学宿舍熄灯之后的争论：美到底有没有标准？一群男生在熄灯的宿舍里争论谁更漂亮，永远吵不出结果——大多数人就此散去，有人却因此把图书馆里所有的美学书都读了一遍，仍然回答不了这个问题。一个念头能不能追问四十年，取决于我们是否允许自己对“没用的问题”认真。25:39｜人生的意义，能不能被证伪？ 一些哲学家说意义是追问意义；意义来自贡献；科学的习惯是去找反例——那些从未追问过、也从未想过贡献的人，人生就没有意义了吗？两种思维方式在这里迎面相撞。也许我们真正需要的不是一个确定的答案，而是一个能被自己的生活反驳的答案。30:54｜当工作不再是刚需：那些什么都不缺的人在追什么？很多人把工作等同于人生的意义，直到有一天工作可以被替代，或者已经多到不再需要。资产的数字越过某个界限之后仍然停不下来的人，TA在追的到底是什么？38:57｜人生的使命与人生的意义：我们常常把两件事说成一件。使命常常不是自己给的，是出生的环境、社会的位置替我们写好的；而意义却只能自己认领。有人一生把使命当作意义，直到很晚才发现两者可以不重合，甚至可以相反。区分它们并不轻松——因为一旦分开，你就必须独自回答“我是谁？我要怎么活？”。46:12｜炯炯有神地航行，却不知驶向何方。如果船不知道要开往哪里，航行还有什么意义？答案是——因为我要在行驶的时候炯炯有神。这大概是对意义最诚实的一种回答：它不承诺终点，只承诺状态。接着被展开的四层结构——基础生存、社会联结、自我实现、超越性——给这句话找到了地基：意义并非只有一种，只是我们在不同的人生阶段，站在不同的那一层甲板上。50:12｜为什么是“联结”：一个主动的动词。relation是继承来的，connection是物理性的，只有联结带着一个动作。这个词的选择本身就是一种立场：意义不是我们身处何种关系的结果，而是我们主动伸手去建立的过程。当一个人不再主动建立联系，他失去的可能不是关系，而是意义感本身。53:45｜从断裂处反推：舍得，才是最难的。 从最极端的情形往回看，一个人要放弃的从来不只是生命，而是他与父母、伴侣、朋友的全部联结——那需要多大的决心。反过来说，我们努力地活着，很多时候是因为有人需要我们。究竟是我们抓住了关系，还是关系一直在托住我们？关系为什么重要？因为里面藏着意义、藏着联结，藏着主体性。01:02:40｜联结意义论的两个哲学基础。马克思 “人是一切社会关系的总和”是静态论断，而哈贝马斯的主体间性——“人与人之间要主动打交道才能相互理解”是动态实践——把这两句叠在一起，意义就不再仅仅是一个人内心的产物。同一个年代、同一碗面、同一段共同经历，在两个人之间长出第三样东西。01:08:05｜爱情作为一种联结在生命当中的意义？它与友情和亲情是一样的吗？所有的亲密关系几乎都是继承来的，而爱情是两个个体间主动建立的——这也是它在现代社会中最脆弱、最难维持的原因。爱情为什么被放在人生意义的最高处，特别是在文学作品里？01:15:24｜爱情带来的伤痛比其他关系更大？我们为什么还要追求爱情？此处引出双南老师的观点——特深连（独特性深层连接）：“共享足够的核心价值；保留足够的个体差异；差异/独特性不被消灭，而成为拓展认知和共同创造的来源。那个天色将暗路灯亮起的时刻，一个说“这是很古典的浪漫”，另一个人抬手看表说“今天路灯亮早了”——同一个傍晚，两种语言，谁也没有让谁改口。我们常常抱怨对方为什么不理解自己，却忘了正是那个不同，才是当初选择他的理由。01:25:00｜好的关系是特深联，不美好的关系有啥共性？从心理学的视角来看，是两个人各自的主体性缺失的程度。爱情在关系中被高举有其复杂的经济与社会原因，其中一个原因是爱情带来的生理唤醒的强度——个关系带来的感受强度和主体性崩塌的程度往往被用于评价这个关系的质量。01:29:21｜痛苦算不算意义？快乐的时候我们几乎不追问意义，只有被按在原地动弹不得时，才开始认真地想。那些被迫写下的思考，最后成了一份由自己书写的人生说明书。01:41:00｜不同年龄阶段理解的人生意义会不同吗？同一个问题，几个人给出了不同方向的轨迹——有人从“爱情大过天”走向“关于我爱且爱我的人”；有人从宏大的使命，一路走回自己、走向联结；有人的人生意义从来都是从关系出发。01:49:51｜“咱们家这么好，我真的不舍得走” 父亲临走前留下的不是对成就的总结，而是对一段关系的不舍。这句话之所以有分量，是因为它把所有抽象的论证一次性落回地面：我们最终在乎的，从来不是活了多久、留下什么名声， 而是我们曾经建立过的那些联结——这算不算另一种永生？如果永生不可能，那么人活着有什么意义？01:54:43｜AI 时代，人何以为人？工具性被拿走之后，人才更像人 。如果人的意义建立在能力之上，那么当能力可以被替代，意义也就随之被消解。但还有一种反向的读法：过去我们是在把自己当机器使用，如果那部分工具化的自己被交出去了，剩下的才是生而为人本来的样子。AI时代，真正的风险不是 AI 太强，是被AI切断人与人的联结，是人不再主动去建立联结——那么，AI时代，如何成就更好的自己？彩蛋：你上一次强烈的感受到自己还活着，是什么时候？00:00｜如何看待与自己的关系和与他人之间的关系？与自己的关系，是所有联结的原点。02:13｜上一次特别感觉到自己还活着，是什么时候？ 同一个问题，答案的差距大到需要现场澄清词义：有人指向与死亡擦肩时那声“我居然还活着”，有人指向每天的呼吸、风和香气。这场小小的争执本身很动人——原来“活着”这个词，在不同人的身体的感受完全不同。08:51｜六岁以前，印象最深的一件事？是山上抓到的那只特别大的蚂蚱，周五爷爷手里那根冰棒。为什么最后要问一个不重要的问题 ？在什么都能被快速获取的时代，愿意花一个晚上共度、并记住彼此的童年，恰恰是全场对“联结”最直接的一次示范。有用的信息越来越易得，那些“没用”的时间反而越来越珍贵。本期书影音推荐《联结意义论》——张双南，本期对谈的文本起点，提出人生意义的四层结构与“特深连”（独特性深层连接）。《在 AI 时代塑造更好的自己：从量子全同性到人类个体差异的教育哲学思考》——张双南。《科学方法与美学》——张双南，中国科学院大学课程教材，获中国科普作家协会优秀科普作品金奖。《真理与美》——钱德拉塞卡（Chandrasekhar），一位诺奖科学家谈真理与美的关系。《别闹了，费曼先生》——费曼。《鲁滨逊漂流记》本期思考这一期把意义安放在了关系里：不是我们拥有什么关系，而是我们在不在主动地伸手。那么，如果把你正在维系的所有联结列一张清单，划掉那些继承来的、不需要你做任何事就存在的——剩下的还有几条？

Our stories tonight are, if I do say so myself, such a treat. If you have ever wished to book a stay at the Inn of Nothing Much and watch the leaves change while sipping coffee from the back porch. If you love trains and journals and cats and trays of food sent up from the kitchen, these stories are for you. Make your bed your favorite place to be this season with Quince. Download the Quince app for app-exclusive offers, or go to quince.com/nothingmuch. Get free shipping on your order and 365-day returns. Now available in Canada and the UK, too. Subscribe to our Premium channel. The first month is on us.

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge

From Saturday morning routines and Father's Day celebrations to beach days with Tate, Perth plans and the latest Princess Polly obsession, Tori is giving us the full life update. Lily's had a BIG week too, from client shows and hiring a new coach to launching Hannah's program and questioning whether pedicures are actually worth it...Plus, we're diving into the Alix Earle documentary because of course we have thoughts, sharing what's been stressing us out lately and chatting through all the usual life updates and chaos.And for this week's recommendation… making your own coconut rice. Trust us, it's underrated.Another Friday episode filled with life updates, business, chaos, opinions and the conversations you'd actually have with your girls.2026 Notion templateRaw Reality https://rawreality.com.au/**https://rawreality.com.au/products/life-upgrade-system-notion-template**Between Us Connection Cards https://rawreality.com.au/products/between-us-cards?utm_source=copyToPasteBoard&utm_medium=product-links&utm_content=webFind us below:@rawreallity https://www.instagram.com/rawreallity?igsh=cjZlMzZsM2lva3hw@ttorisstory https://www.instagram.com/ttorisstory/@fitwithlilyy https://www.instagram.com/fitwithlilyy/Community Facebook group https://www.facebook.com/share/v3sbEonnywyvp1h7/

In this round, Nadeem wants to know if famed Spanish director, Pedro Almodóvar is hot or not. Starting this week with the 2013 comedy, "I'm So Excited!" for the next movie review. Mita and Nadeem try not to fight about Lindsay Clancy

Nick Jones joins Stu for a wide-ranging conversation about Nick's cycling journey. Nick shares his story growing up in Southern California, finding football and work ethic, drifting through his early 20s, and then rebuilding his life through faith, family, and school. They dive into his COVID-era entry into cycling, LOTOJA experiences, obsession with stats and heart rate, sentimental streak, balancing investment banking with training and family life, and the simple mantra that guides him now: there's always more speed on the other side of pain.

“我的疗愈真的是从自救开始的，所以自救就成了我的一个路径依赖。”——麦啾先要承认一件事：疗愈这个词，是这几年才有的。但疗愈这个动作，很多人在没有这个词的年代就已经在做了，只是那时候它叫别的名字——叫挨过去，叫熬，叫自救。这一期三个小伙伴和哲学爱好者麦啾、常驻嘉宾乔乔从各自的起点讲起：高一那年清早自动流下来的鼻血；大一暑假去世的爷爷和一句“学习哲学能自救”；两次婚姻结束之后，在一盆水里泡了大概两秒、却觉得泡了四十年的瞬间；父亲离世、爱人离开，那种不疼但觉得人生是一片荒原的空；妈妈去世后把最不祥的鸟纹在背上…于是疗愈方法论千奇百怪。有人神农尝百草：从北医六院、广济寺到西什库教堂，从运动、做好事、写日记一路试到儒家和金刚经；有人靠战略性思维，两害相权取其轻，把一段有毒的感情当断臂来断；有人靠“用下一个胜利重写上一次伤痛”，在越野跑的压线冲刺里把痛苦转化成动能。于是一个值得问的问题涌现：如果一个人反复地、颗粒度极细地检视自己的每一道疮口，TA到底是在愈合，还是在把伤口放大？“我变好了”这件事，是不是不知不觉变成了一项必须完成的任务？麦啾说，试图消灭痛苦，反而会陷进“要消灭它”的焦虑里；能和痛苦共处的那一刻，其实是你的内在空间变大了。乔乔说，咨询之所以起效，无关技术也无关流派，核心是关系——一个人自我受损的程度足够大时，他极难独自把自己拼回来。而这一期最动人的转身发生在最后：一个自救了二十多年、把自救当成路径依赖的 INTJ，在收尾时说她今天最大的收获是“关系”。疗愈从来不通往“我终于好了”这个终点。它通往的，是你终于有权利决定，此刻的你想怎么活。时间轴03:18｜在没有“疗愈”这个词的年代“疗愈”要先从“这不是什么”说起：不是产业，不是身心灵生意，也不是某个行当的专业术语。当一个词被商业和话术反复使用，一个人要谈论自己真实的痛，反而得先把它从别人手里抢回来。而这个动作其实一直都在——只是从前它叫别的名字：叫熬，叫挨过去，叫自救。自救和疗愈之间隔着什么？07:25｜没有人来救的那条胡同一个十五岁的女孩，早上醒来鼻血就流下来；回家说给父母听，得到的是一个合理、体面、也确实为她着想的答复。最难的往往不是伤害本身，而是发现周围所有人都没做错什么，而你依然无处可去。两年后她才知道，当年有人在她看不见的地方给她定了性，也有人不动声色地把那句话抹掉了——如果真相当年就到了，她承受得起吗？11:49｜神农尝百草：一个人的病急乱投医心理诊所、精神科、药物、哲学系、寺庙、教堂、运动、写日记——她并不知道哪一样有用，但一样都不肯放过。药只能把人从溺水边缘捞到“仍然在水里”；剩下的路，得靠那些能持续几年的小事一寸寸走。病急乱投医里其实藏着很硬的东西：不肯就这样算了。24:32｜在一盆水里泡了四十年一个怕水三十多年的人，把自己整个沉进一盆水里两三秒，浮起来时觉得所有不堪“好像也没什么”。是幻觉，还是真的被饶恕？他至今不确定，也不打算确定。有些疗愈发生在解释之外——无法证实，也无法证伪，只能说那是一个体感。28:20｜起点不是太痛，而是感觉不到痛疗愈未必始于痛到活不下去，也可能始于不痛——只是空，只是觉得人生是一片荒原。这种状态更不容易被看见，因为它不流血也不喊叫。往外走的第一步有时近乎粗暴：先把脏水泼回给该负责的人，再把那个留在原地的小孩抱起来。一个人如果始终认为一切都是自己的错，他连开始的资格都没有。33:36｜战略性思维：把伤口意义化，是解药还是麻药两害相权取其轻，把一段有毒的关系当断臂来断——这是一种非常有效的本领。它还有另一面：把痛苦里的一切都赋予意义，痛苦好像就淡了。可意义化究竟是消化，还是一种更体面的回避？40:25｜松动：我变好了不是目标当“我好了”变成一项要完成的任务，完成后的那种解脱感，究竟是愈合，还是交差？说出这个怀疑，几乎像是对一段长久陪伴的背叛。但答案可能恰恰相反：一个好的陪伴者高举的从来不是方法本身，而是你有权定义什么对你有用。47:50｜托底的信念：善是绝对的，恶只是缺乏相信最终的善在那里等着自己的人，不会彻底绝望。这信念无法被证明，却像地基一样撑住上面所有的方法论。有意思的是它还需要一个反面来配平：不断向君子靠拢，也就不断苛责自己；承认人本有局限的那一刻，人才不至于跟自己死磕到底。54:01｜将心来：痛苦的主体脱落了慧可说自己心不安，达摩说，那你把心拿来。读到这里，一个正在抑郁状态里的人说痛苦立刻少了六成——不是想通了什么道理，而是承受痛苦的那个“我”被抽掉了一瞬间。所以向经典要的从来不是答案，是力量：有些话你当时不懂，先收着。57:16｜见众生：你并不是独自在受苦真正的见识不是去过多少高处，而是见过多少种普通人的活法。看别人怎么活，会消解一种隐秘的自恋——你并没有独自忍受这世上所有的痛苦，你也没有那么特殊。而不特殊，本身就是一种松弛。01:04:45｜关系：一个人拼不回自己咨询真正起效的，是技术、流派，还是坐在对面那个人愿意与你建立的关系？把答案押在关系上，等于承认了一件不太好承认的事：每个人的终极使命是成为他自己，可主体性没办法自己跟自己确认，它必须在某段关系里被照见。哪怕那是和一只猫、一本经典、路上遇见的众生。01:27:54｜消灭它、共存、还是转化它三条路各有出处，但真正身在痛苦里的人说，那一刻根本没有这种分类。想消灭它的，会掉进“必须消灭它”的新焦虑；想转化它的，本质上还是在跟它较劲。唯一不较劲的那条也许是：当你真能和它共处，说明你的内在空间已经大过了它。01:35:01｜背上的乌鸦和塞不进地下室的大象习惯把痛苦塞进地下室的人，总会遇到一头塞不进去的大象。她的解法都不太理智——抄经，把家乡最不祥的象征纹满后背——逻辑却很硬：最坏的事我自己背上了，你还能拿什么伤害我？后来她从消灭走到共存，也不是因为想通了，而是因为它就是消灭不掉。01:41:27｜托底方案：把心理咨询当成一个外挂当自己的智力、情绪、身体、物质资源全部用尽，仍然穿不过去的时候，外部支援的位置才出现。它的价值不在于比你聪明，而在于它不是你自己——一个足够大的自我受损，极难由这个受损的自我独力拼回来。但外挂有前提，也有赝品：前提是先遇到那个适合你的人；赝品是那些三天就承诺治好你的生意。01:47:47｜从自救到关系：一个 INTJ 的转身把自救练成路径依赖的人，二十多年里几乎把关系带给她的疗愈价值整个忽略掉了，直到她在一群持续关注她的人那里，看见爱被具象化。“本自具足”没有错，只是本自具足的人，也依然需要有人在旁边看着她。本期书影音推荐《治疗文化》——Frank Furedi《个体形成论》——卡尔·罗杰斯，乔乔的核心参照：“咨询起效的，或者叫咨询有价值的核心的点，就是咨询关系本身”。《金刚经》——朋友对麦啾说它“能够破一切苦厄”；她每天上班路上读四十分钟，直到读懂“因无所住而生其心”。《心经》禅宗公案「二祖慧可求心安·将心来」“牢骚太盛防肠断，风物长宜放眼量”“反者道之动”——麦啾反复引用的道家表达，对应她的"Nothing is permanent"。本期思考这一期其实提出了一种很朴素的疗愈观：人不是一台需要被修好的机器，而是一个需要被看见的空间。那么——当你说“我想变好”的时候，你想变好的那个“好”，是你自己定的，还是别人替你定完之后交给你的？而如果疗愈根本没有“毕业”的那一天，没有一次性的痊愈，只有不断跌落、又不断重新看见自己，你还愿意继续做这场功课吗？

Al and Codey talk about all the latest news. Timings 00:00:00: Theme Tune 00:00:30: Intro 00:05:13: What Have We Been Up To 00:20:07: I Know What You Released Last Month 00:22:49: Game News 00:43:58: New Games 01:19:53: Outro Links Pokopia PokemonXP Cloud Island Moonlight Peaks 1.2.6 Update Story Of Seasons / Rune Factory Livestream Farming Simulator 25 Beans & Alpacas Expansion Petite Planet Trailer Research Story on Switch Haunted Chocolatier Info Keep Watering Farm And Feast Hammer And Harvest: Dwarven Roots Isle Of Food Meadgard Dungeons And Dragons: Long Rest Tavern Animula Nook Spirit Rancher Halcyon Days At Taoyuan Gaucho Origins Contact Al on Mastodon: https://mastodon.scot/@TheScotBot Email Us: https://harvestseason.club/contact/

The AP's Marcela Sanchez has the Labor Department's July job openings report.

What makes a chord cool? How do we go beyond the 12 notes and into frequencies? And how do we avoid all that nasty music theory getting in the way of our artistic selves?Well, I brought my mini-piano to explore just that. Let's dive in.For 30% off your first year with DistroKid to share your music with the world click ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠DistroKid.com/vip/lovemusicmore⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Space is so big it almost doesn't feel real. You sit with that for two seconds and then the rest of the week hits you in the face.This week we talk about Dolly Parton passing away — and why that one actually lands different — a monkey that walked out of a lab in Japan, lived its best life for 24 hours, and then just… came back, flash floods that turned beautiful places into disaster zones overnight, and a whole bunch of other stuff that had no business being in the same conversation.Unfiltered. Slightly unhinged. The kind of episode where you start talking about the size of the universe and somehow end up arguing about a monkey with better decision-making than most adults.It's messy. It's relatable. It Gotta Get Said.

This week we discuss the changing landscape of sports ownership. Have sports owners changed over the years or have they just become different for better or worse?For our This Week In Baseball, we discuss the firing of Danny Ozark. Would the Phillies have succeeded with him still at the helm or did Dallas Green have the necessary magic?Our Hall of Famer this week is pitcher Addie Joss. The ten year eligibility requirement was waved for his induction, but were his numbers enough to warrant this waving or is this a head scratcher?In our singlular parting shot this week discusses the career of Tommy John as well as his sport altering surgery.Enjoy our new crop of weekly commercials featuring Mel Allen for baseball cards, Baseball Hall of Famers for Mastercard, and Bugs Bunny and Sam for Post Cereal baseball cards.Please join us as we discuss baseball topics and we continue our mission to make The Hall small. We hope you'll enjoy the ride.TimestampsThis Week In Baseball - 21:21Hall of Fame Discussion - 41:55Parting Shot - 1:02:22

Barred owl removal is moving forward in California and new data suggests that removals are working as intended: freeing up northern spotted owl habitat that was lost to barred owl invasion. A new peer-reviewed paper in the journal Biological Conservation used GPS locators on 56 barred owls in Northern California confirms that barred owls preferentially choose the same older forests that spotted owls require. Slightly larger than spotted owls, barred owls are able to bully spotted owls from their nest sites. Now, barred owls are pervasive across the landscape and the consequences are dire: just this week, another new paper described the spotted owl as "functionally extinct" across much of its range in Washington and Oregon. But through sustained removal efforts, wildlife managers are able to recover substantial amounts of spotted owl habitat, improving the prospects that your kids or grandkids can enjoy a world with northern spotted owls. Vitek Jirinic of the Integral Ecology Research Center join the program to discuss. Support the show

The housing market is going through another shift and entering a new phase. While not radical it may change your strategies on buying, building, or selling your home. David brings up some new observations about what is occurring with home sales, inventory, and buyer behavior.Special thanks to our special new sponsor EchoShatter Band

Inflation has cooled slightly in new figures, which show prices rose 3.5 per cent in the 12 months to July

Inflation has cooled slightly in new figures, which show prices rose 3.5 per cent in the 12 months to July

As you grow as an engineer, something counterintuitive happens: the systems you build get simpler, not more complex. In this episode, I explore why senior engineers tend to collapse abstractions, accept certain risks, and reduce the surface area they're responsible for — and what drives that shift underneath the surface. Understanding the reasons behind the trend is what lets you get there on purpose instead of waiting a decade to arrive there accidentally. Early in your career, output goes up fast. You're building more than ever, especially with AI in the mix, and a lot of what you build carries real complexity. But watch engineers who have been doing this a long time and you'll notice the opposite of what you'd expect: as their craft improves, their code, their architectures, and their systems get simpler. In today's episode, I dig into why that happens — and why chasing simplicity directly is less useful than understanding the underlying forces that produce it. Simplicity Isn't the Goal — It's the Byproduct: Trying to "make things simpler" as a directive doesn't get you very far. If you can understand the core reasons complexity tends to fall as engineers mature, you can aim at those reasons instead and arrive at simplicity organically. Why Simpler Code Pays Off: Simpler things are easier to understand, which means junior engineers can pick up your code, and future-you can return to a project you've long since left and still make sense of it. Adapting a simple thing is almost always easier than adapting a complex one. The Pain of the Refactor Teaches You: A big part of this shift is scar tissue. Once you've lived through a massive refactor of a complex system, you start making different trade-offs — not from theory, but because you don't want to do that again. Collapsing Vertical Abstractions: One of the most common refactors senior engineers reach for is collapsing long chains of abstraction that only ever get used in one place. It's abstraction without reuse, and it forces you to re-load enormous context just to make a small change. Refining Your Risk Tolerance: Early on, we hedge against every possible risk. Later, we learn some risks are acceptable. Hedging is insurance, and sometimes it's very expensive insurance — paid in velocity, in onboarding difficulty, and in only being able to hire people who can hold all that complexity in their heads. Knowing When to Break Best Practices: Maybe the best practice says abstract this. But if it isn't that hard to understand, and you can get most of the benefit through better naming or tighter scoping, the "correct" move might be the wrong one. The Library Trap (In Both Directions): Seniors often take a trip through "let's not use external packages, we don't know what's in them" — and end up maintaining a shadow version of the thing they avoided. The more experienced call is often to accept the trade-off, adopt the well-documented dependency, and shrink what you are responsible for. Reducing Surface Area Creates Focus: The through-line in all of these trade-offs is a shrinking surface area. Fewer things to maintain means more focus, and more focus means the things you are responsible for get done very well. It's an open question whether those behaviors follow seniority or cause it. Drive to the Fundamentals: Think about a machine built from a ramp, a screw, a rubber band, and a motor. A more senior craftsperson recognizes the problem is fundamentally about conservation of energy, and reconfigures it down to two parts instead of eight. Slightly less efficient, maybe — but far less to teach, maintain, and break. Episode Homework: Ask yourself: what am I responsible for right now that I could simplify? Where can I get away from the tactics and the surface-level stuff, get down to the core of the thing, and focus on doing that core really well?

Should you ever repurpose an old post on LinkedIn?Short answer: yep.Slightly more annoying answer: it depends.Before you copy, paste, and send that post back into the wild, figure out why it flopped the first time. Was it actually a good post the algorithm didn't push out? Or was the post itself a little off? Those are two very different problems.In this Story Snack, Stacy gets into how she decides which posts deserve another shot, which need a tweak first, and which she'd leave alone.Listen in to hear:The one reason NOT to repost that Stacy isn't buyingHow to distinguish a reach problem from a writing problemWhen a few tweaks will do, and when you need more than thatWhy her short Tuesday posts aren't just chopped-down versions of longer onesWhat working with the LinkedIn algorithm right nowThis is Story Snacks: a bite-sized series for fund managers who want to get better at strategic storytelling in under 20 minutes a week. ---Running a fund is hard enough.Ops shouldn't be.Meet the team that makes it easier. | billiondollarbackstory.com/ultimus

Is it just me, or is every single conversation right now about dating apps, attachment styles, and what the hell is even happening between men and women anymore? I've been on a hell of a journey these past couple of years thinking about this, and I finally sat down with someone who says the things most of us are thinking but are too scared to say out loud. I came across Emma years ago, this amazing artist, poet, and singer-songwriter who is so integrated in her dark feminine that this conversation went everywhere, and I personally did not want it to end. In this episode I explore:

In an interview conducted on May 4, 2026, Laura Silver joins Steve Orlins to discuss the data on how American perceptions of China have changed over the past year. Americans' views toward China are changing, according to a recent Pew Research Center report published this April: today, 27 percent of Americans have a positive opinion of China, up six percentage points from last year and nearly double the share from 2023.  Amid several notable findings in the survey, some of the most striking are:  Confidence in Chinese President Xi Jinping to do the right thing regarding world affairs has increased four percentage points since last year and has roughly doubled since 2023.  When asked whether China is a partner, enemy, or competitor of the United States, fewer Americans now describe China as an enemy than in 2025, though most still view it as a competitor.  Slightly fewer Americans now than last year say China benefits from trade at the expense of the United States.  About the speaker: https://www.ncuscr.org/event/americans-shifting-views-on-china-in-recent-years/

This episode is presented by Create A Video – Kush Desai, Special Assistant to the President and Deputy Press Secretary, joins me to talk about the latest CPI economic data that got released today. Plus, the social security trust fund is set to run out of money by 2032. This means the US Senators we elect this year will be part of the Congress that has to face the crisis. Choose wisely.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-pete-kaliner-show--6946691/support.Subscribe to the podcast My preferred podcast platform: SpreakerAll the links to Pete's Prep are free!Get exclusive content here!Media Bias Check: GroundNews promo code!Advertising and Booking inquiries: Pete@ThePeteKalinerShow.com  

Our stories tonight treat us to some of the loveliest moments of late summer. They are about overflowing gardens and farmers market stalls, heavy rains and nights around the bonfire. They are also about blankets stretched out on the grass, flowering plants and old stately trees, and the sweet moments that come before the fall. Subscribe to our Premium channel. The first month is on us.

Mike and his wife have started to realize their daughter has a million questions about transitioning into to Middle School that they would have never even thought about. For example. Recess is gone! This was an impromptu podcast during one of those conversations. See omnystudio.com/listener for privacy information.