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Harvard Adds AI Avatars to a $699 Founder Bootcamp
Harvard Business School has put an artificial-intelligence twist on entrepreneurship training, rolling out a new eight-week startup bootcamp that pairs live instruction with AI-generated instructor avatars. The program, known as HBS Foundry, costs $699 and is designed to give aspiring founders more personalized feedback during pitch practice and simulated board meetings.
According to reporting from TechCrunch, the course uses avatars built by AI video platform HeyGen to mimic instructors and respond during exercises such as practice pitches and boardroom scenarios. The weekly live sessions are still led by real teachers, but the AI avatars are intended to extend the feedback students receive between those classes.
The idea reflects a broader push by business schools and online learning platforms to make startup education more scalable without losing the feel of individualized coaching. In this case, Harvard is trying to blend human-led teaching with digital replicas that can deliver critiques in a format similar to a live conversation.
The bootcamp is part of Harvard Business Schoolâs effort to reach founders beyond its traditional degree programs. HBS Foundry is aimed at entrepreneurs who want practical guidance on shaping and testing ideas, refining their pitches and preparing for investor-style questioning. By using AI avatars, the program attempts to offer more frequent and accessible feedback than a standard classroom model might allow.
CryptoRankâs coverage highlighted the novelty of the setup, and the story quickly spread across tech and startup media because of the contrast between Harvardâs elite brand and the mass-market feel of a $699 online bootcamp. The price point is notably lower than the cost of many executive education offerings, which makes the course more accessible to early-stage founders and operators.
The use of AI avatars also raises questions that are now common across education and corporate training: how well can synthetic instructors capture the nuance of a seasoned mentor, and where is the line between useful automation and imitation? In this program, the avatars are not replacing live faculty altogether, but they are taking on a role that traditionally depends on one-on-one human interaction.
HeyGen, the company behind the avatars, has been positioning itself as a tool for AI-powered video creation and presentation workflows. In Harvardâs bootcamp, its technology is being used for a more specific purpose: simulating instructor feedback in a startup-training environment. That makes the course one of the more visible examples so far of generative AI moving from demo use cases into formal education.
The broader significance lies in what Harvard is signaling about the future of teaching entrepreneurship. If AI avatars can reliably provide structured feedback on pitches, board meetings and founder communication, similar systems could be adopted by other schools, accelerators and corporate training programs. If they fall short, the experiment will still serve as a useful test of how far AI can go in roles that depend on judgment, tone and mentorship.
For now, HBS Foundry stands out less as a replacement for professors than as a hybrid model that uses software to extend their reach. That combination of live instruction and AI-driven personalization is likely to draw attention from founders looking for practical training â and from educators watching how far the technology can be pushed.
Around the world, August 18, 2026 is marked by an overlapping set of security crises, geopolitical standoffs and significant political developments, from the RussoâUkrainian war and U.S.âIran tensions to domestic upheavals and elections in Africa and Asia. Escalation in the RussoâUkrainian War and UKâRussia Tensions The RussoâUkrainian war remains at the center of global concern, with both the battlefield and the diplomatic arena showing signs of heightened confrontation. Russia reports that Ukraine has launched a massive wave of drone strikes, with more than 600 drones sent toward Moscow and surrounding regions overnight, injuring several people and underscoring Ukraineâs expanding longârange capabilities. These attacks are part of a broader pattern of Ukrainian operations targeting Russian infrastructure, aiming to disrupt logistics and signal that rear areas are no longer safe. The strikes have triggered an increasingly sharp war of words between Moscow and London. Russian officials have warned the United Kingdom of unspecified âconsequencesâ following reports that Ukraine has used Britishâmade drones to strike deep inside Russian territory. In response, the British government has reaffirmed that it will continue military support for Kyiv and framed such assistance as part of its commitment to help Ukraine defend itself against what it calls Russiaâs illegal invasion. The dispute highlights how the conflict has evolved into a broader confrontation between Russia and NATO members, even as Western governments seek to avoid direct military escalation. On the ground, civilians continue to bear the brunt of the hostilities. A Russian ballistic missile strike on Pechenihy in Ukraineâs Kharkiv Oblast has reportedly killed ten civilians and wounded eight more, with several homes destroyed. The incident underscores the persistent vulnerability of communities near the front lines and the difficulty of protecting civilian infrastructure from longârange strikes. U.S.âIran Tensions and Threats Involving Oman In the Middle East, the expiration of a 60âday U.S.âIran peace or truce framework has renewed fears of escalation in and around the Strait of Hormuz, a vital chokepoint for global oil shipments. The deadline, stemming from a memorandum of understanding reached in June, has passed without a broader agreement, prompting Iranian officials to warn that they may shift to a more offensive posture if diplomacy fails. Such a move could include steppedâup naval activity or proxy operations that threaten commercial shipping. The situation has been further inflamed by remarks from U.S. President Donald Trump, who has reportedly threatened to bomb Oman, a key U.S. ally and traditional mediator in Gulf disputes, if it were perceived as obstructing Washingtonâs efforts to shape a new arrangement with Iran over the Strait of Hormuz. These threats represent an extraordinary public hardening of rhetoric toward a friendly state and risk complicating regional diplomacy, given Omanâs historic role as a discreet channel between Iran and the West. Markets and policymakers are closely watching the implications for global energy security. Analysts note that the expiration of the truce and the renewed threats coincide with increased volatility in sovereign borrowing costs and wider concerns about geopolitical risk. Shifting U.S. Military Posture in East Asia U.S. policy toward East Asia is also undergoing visible change. President Trump has ordered a significant scaling back of joint U.S.âSouth Korea military exercises, describing them as needlessly hostile to North Korea and citing his personal relationship with North Korean leader Kim Jongâun. He has also criticized Seoul for what he characterizes as insufficient support for U.S. efforts regarding Iran. In South Korea, these moves are feeding a domestic debate over the countryâs longâstanding security arrangements with Washington. President Lee Jaeâmyung has reiterated his desire for full operational command of South Korean forces to return to Seoul, framing this as an essential step toward greater military independence. The combination of reduced exercises and Seoulâs push for more control raises questions about the future shape of the alliance and regional deterrence against both North Korea and China. Violence and Security Crises in Asia and the Middle East Beyond major power confrontations, a series of violent incidents and structural crises highlight the fragility of security in several regions. In the Philippines, a school shooting at Ateneo de Zamboanga University in Zamboanga City has left one student dead and nine others injured; the alleged perpetrator, also a student, died by suicide after the attack. The tragedy has intensified scrutiny of campus security and access to firearms in a country already grappling with periodic political and criminal violence. In Syria, an explosion at a thermal power plant in the coastal town of Baniyas has killed three people, underscoring the ongoing risks to critical infrastructure in a country still recovering from years of civil war. Meanwhile, in Indiaâs Madhya Pradesh state, at least eight people have been killed and more than 20 injured after a vehicle rammed into a truck, adding to the toll of road accidents that remain a persistent public safety issue. The Middle East faces parallel humanitarian and political pressures. In Lebanon, the humanitarian situation has sharply deteriorated following the collapse of a ceasefire between Israel and Lebanese actors earlier in the summer. Casualties and damage to civilian infrastructure have mounted, and displacement has surged as the United Nations Interim Force in Lebanon (UNIFIL) approaches a scheduled end to its mandate later in the year. Observers warn that the drawdown of UNIFIL, combined with escalating violence, could push the country into deeper instability and complicate any future diplomatic settlement. Political Shifts and Regulatory Responses Amid these security crises, several notable political and regulatory developments are shaping domestic landscapes. In Zambia, President Hakainde Hichilema has been declared the winner of the general election, securing a second term in office after a tumultuous campaign. His reâelection is being closely watched for its implications for economic reform, antiâcorruption efforts and regional diplomacy in southern Africa. In Pakistan, the Supreme Court has ordered that former prime minister Imran Khan, currently imprisoned, be transferred from a central jail in Rawalpindi to Shifa International Hospital in Islamabad for medical examinations following a petition by his lawyers regarding his health. The decision underscores how legal battles around highâprofile political figures remain central to Pakistanâs turbulent political scene. In Singapore, authorities have begun implementing strict new antiâscam rules targeting major internet messaging platforms and social media services. Under the regulations, companies must restrict unsolicited contacts, verify advertisers and block suspected scam advertisements, with potential penalties of up to S$1 million for nonâcompliance. The move reflects a broader global trend of governments asserting tighter control over digital platforms in response to fraud, misinformation and consumer protection concerns. Outlook The mosaic of events on August 18, 2026 illustrates how local and regional crises are increasingly interconnected. Drone warfare in Eastern Europe, shifting U.S. military commitments in Asia, renewed tensions in the Gulf, domestic political battles and regulatory crackdowns in the digital sphere all feed into a complex and often unstable global landscape. Policymakers, markets and citizens alike face a period in which decisions in one theater can quickly reverberate across continents, amplifying both risks and the need for coordinated diplomacy.
AInews: Universities Put Artificial Intelligence to the Test in Classrooms and Exams On August 25, 2026, universities from the United States to Europe accelerated experiments with artificial intelligence in teaching and assessment, turning AInews into a daily reality for students as institutions test new rules, tools and ethics frameworks for higher education. How are universities testing AI in everyday student work? Universities are moving from adâhoc experimentation to structured pilots that build AI into normal coursework, rather than treating it purely as a cheating risk. Students are being asked to use tools like ChatGPT for assignments under clear disclosure rules, while some institutions now embed AI literacy courses before granting access. Recent experiments and policies include: According to aiX Weekly, dated August 19, 2026, the University of Colorado Colorado Springs (UCCS) opened ChatGPT Edu to all students on August 14, but only after they complete an AIâliteracy module in Canvas. According to the same aiX Weekly report, account provisioning at UCCS requires students to pass a short course covering prompt design, bias, hallucinations and data privacy. According to a Deseret Magazine feature from August 22, 2026, several U.S. universities now maintain a âlaneâ where professors design assignments that assume students will use generative AI, focusing grading on reasoning and source evaluation instead of raw text production. According to HumanizeThisAIâs March 18, 2026 policy survey, most accredited institutions now follow a âfollow your instructorâ framework where course syllabi specify whether AI is encouraged, restricted or prohibited for each assignment. These pilots share a pattern. AI is treated as a tool students must learn to handle critically. Policies require explicit acknowledgement of use, and instructors redesign tasks to assess judgment, not typing speed. What changes are being made to exams and assessment design? Assessment is where AI forces the largest redesign. Leading universities are testing handsâon, oral and projectâbased formats that make unauthorized AI use harder and move grading toward process, collaboration and application, rather than finished prose that a chatbot can generate. According to an MITâlinked report covered by Forbes on August 25, 2026, a committee at the institute warned that generative AI can now credibly complete most typical undergraduate assignments. According to that same MIT report, recommendations include more inâclass work, practical labs, and assessments that require students to critique AI outputs, not just produce text. According to Deseret Magazine on August 22, 2026, some universities experiment with dualâstage assignments: students submit an AIâassisted draft, then revise it in class without devices, allowing instructors to compare the two versions. According to the May 15, 2026 Weekly AI in Higher Education report from the Learning Research and Development Center, the EU AI Act classifies AI used for student assessment, admissions screening and progress monitoring as âhighârisk,â requiring human oversight and transparency by August 2026. These moves respond to a practical reality. AI is strong at formulaic essays and problem sets. Assessment design now aims to test understanding that cannot be easily outsourced: oral explanations, original data analysis, and collaborative projects grounded in verifiable sources. Are universities still relying on AI-detection tools to police cheating? Use of AIâdetection software is falling as universities question its accuracy and fairness. Many institutions now emphasize disclosure rules and assignment redesign over trying to âcatchâ AIâgenerated text, and some have formally disabled detection features in major plagiarism platforms. According to AHigherVisionâs AI in Higher Education Daily Brief on August 27, 2026, the University of Nevada, Reno stopped relying on AIâdetection software as part of a broader reconsideration of its response to generative AI in coursework. According to HumanizeThisAIâs March 18, 2026 survey of university AI policies, at least 16 institutions had disabled Turnitinâs AIâdetection feature, with more expected to follow as renewal dates arrive in 2026. According to the same HumanizeThisAI report, the dominant approach is a syllabusâbased disclosure requirement combined with guidance on acceptable and unacceptable AI help, rather than full prohibition. Faculty complaints about false positives and biased detection against nonânative writers pushed this shift. Universities now argue that fair assessment must rest on transparent expectations and safer assignment design, not opaque algorithmic judgments about authorship. What new governance frameworks are shaping AI use in higher education? Major university systems are moving to systemâwide governance frameworks that set deadlines for local policies, mandate training and embed dataâprotection and biasâevaluation requirements. These frameworks aim to replace scattered courseâlevel rules with consistent obligations across teaching and research. According to the Weekly AI in Higher Education report released May 8, 2026, the State University of New York (SUNY) board adopted a systemâwide AI policy that requires all 64 campuses to create or update AI guidelines by December 31, 2026, with a possible twoâmonth extension. According to EdTech Magazine on June 26, 2026, the SUNY policy demands training on safe and responsible AI use for campus stakeholders, clarifies roles and responsibilities, and adds procurement safeguards to protect institutional data. According to the May 15, 2026 Weekly AI report, European universities face an August 2026 compliance deadline under the EU AI Act for highârisk educational AI systems, including tools used for grading and admissions. According to aiX Weeklyâs August 12, 2026 issue, EDUCAUSE released an AI literacy framework for higher education during spring 2026, outlining core competencies in critical evaluation, ethical use and technical understanding. These governance measures treat AI as an institutional infrastructure issue. They tie academic integrity, data protection and civilârights obligations together, making registrars, CIOs and provosts jointly responsible rather than leaving AI to individual instructors alone. How widespread is AI use among students and faculty now? Survey data show AI moving from curiosity to routine habit in higher education. Weekly use now reaches a majority of respondents in recent polls, and daily use is at its highest level since generative tools first entered campuses in early 2023. According to an August 25, 2026 briefing from AACRAO, weekly AI use in higher education exceeds 50 percent among surveyed students and staff. According to the same AACRAO report, daily use reached its highest level since spring 2023, when early ChatGPT experiments began on many campuses. According to aiX Weekly reports through August 2026, faculty adoption has shifted from isolated early adopters to departmentâlevel initiatives, such as standardized AI assignment templates and shared literacy materials. AI is becoming part of the background of study life: used for drafting emails, checking code, summarizing readings and generating study questions. Policies now aim to regulate that ordinary usage rather than pretending it does not exist. What are leading institutions like MIT proposing for the future of college? MIT and peer institutions argue that generative AI forces a rethinking of core undergraduate structures. Their committees recommend redesigned curricula, new roles for handsâon learning and clear, courseâspecific AI rules embedded in syllabi rather than generic bans. According to the Washington Postâs August 25, 2026 coverage, an MIT committee warned that generative AI now credibly completes most standard undergraduate assignments, creating âmassive, longâterm disruptionsâ in education. According to Forbes on August 25, 2026, MITâs report calls for more projectâdriven courses, explicit AI usage policies per class, and assessments that ask students to interrogate AIâgenerated content as part of learning, not just avoid it. According to AHigherVisionâs August 12, 2026 brief, MIT also released a governance package for scholarly content used in training generative models, with rules for consent, citation and optâouts. These proposals frame AI not only as a tool but as a structural force. If chatbots can handle routine work, MIT argues colleges should focus more intensely on creative inquiry, lab experimentation and publicâinterest applications that demand human judgment. What new academic programs and roundtables are emerging around AI ethics and literacy? Higher education leaders are building new programs that treat AI itself as a subject of study. Institutions launch minors in critical AI studies, convene roundtables on assessment reform and embed mandatory literacy courses for incoming students. According to aiX Weekly on August 26, 2026, Oberlin College will start a Critical AI Studies minor in fall 2026, focusing on ethical, cultural, environmental, political and labor effects of AI. According to ETEducationâs report on a Pearson roundtable held August 7, 2026 in Hyderabad, higher education leaders there discussed assessment reform, faculty transformation and experiential learning in an âAIâenabled future.â According to AHigherVisionâs August 12, 2026 brief, Cornell University plans AI literacy requirements for all incoming students, integrating critical use of generative tools into general education. These initiatives mark a shift from treating AI as a narrow technical topic. They embed questions of power, labor and culture into the curriculum, so graduates can evaluate not only how to use AI, but whether and under which conditions it should be used. Who is most affected by the rapid expansion of AI in higher education? Students, faculty and administrators all experience the effects of AI experiments, but in different ways. Students face shifting rules between courses. Faculty confront pressure to redesign assignments quickly. Administrators manage compliance, procurement and public trust. Students: According to AACRAOâs August 25, 2026 data, over half of surveyed students now use AI weekly, which means policy changes affect daily study habits. According to HumanizeThisAIâs March 2026 policy survey, AI rules can change from class to class within a single semester, depending on each instructorâs stance. Faculty: According to aiX Weekly issues across August 2026, instructors are expected to articulate AI expectations in syllabi and to participate in literacy training themselves. According to the Pearson roundtable report, faculty transformation and support were central themes for leaders worried about workload and training gaps. Administrators: According to the SUNY policy analysis in EdTech Magazine, CIOs and registrars must balance fast adoption with dataâprotection and bias safeguards embedded in procurement. According to the May 15, 2026 Weekly AI report, European university leaders must classify systems under the EU AI Act and document humanâoversight procedures, or risk nonâcompliance. Across these roles, pressure mounts to act quickly without sacrificing fairness. The pace of AI tool development keeps increasing, while legal and ethical requirements grow stricter. Universities are learning in public, with students watching closely.
On July 21, 2026, Google rolled out Gemini 3.6 Flash across its developer stack, and the AInews community spotted the new model running inside the Antigravity IDE days before the company fully documented the change. The rollout turns 3.6 Flash into the default engine for Googleâs agentic coding tools. What exactly is Gemini 3.6 Flash and when did it arrive? Gemini 3.6 Flash is Googleâs latest âfast-and-cheapâ large language model tier, released on July 21, 2026 as a general-availability upgrade to Gemini 3.5 Flash. It focuses on cutting token costs and latency while improving coding, knowledge work and multimodal tasks, and it launched the same day across Antigravity, the Gemini API and related developer products. Key release facts gathered from Google documentation and independent technical blogs paint a clear timeline: Release date: According to Googleâs Gemini Enterprise model catalog, Gemini 3.6 Flash reached GA on 21 July 2026 . Coverage: A developer-focused blog reports the model went live simultaneously in the Gemini app, Google Antigravity, AI Studio and Android Studio on the same day. Knowledge window: That blog notes the knowledge cutoff advanced from January 2025 to March 2026 , a 14âmonth jump, giving the model fresher technical and product data. Context length: The same source cites a context window of over 1 million tokens , with maximum output around 65,536 tokens . Googleâs own API changelog describes 3.6 Flash as a âworkhorseâ tuned for more efficient reasoning and tool calls, targeting long-running coding and agent workflows rather than short chat prompts. How did Gemini 3.6 Flash first appear inside Antigravity? Gemini 3.6 Flash surfaced in Antigravity before most users saw formal documentation, after testers noticed a new model ID in the interface and shared screenshots on social media. Those early sightings triggered days of informal testing while Google iterated on the backend and finalized public release notes. Evidence of this staggered emergence comes from several independent sources: A leak-focused blog reports that a model identifier âgemini-3.6-flash-tieredâ appeared inside Antigravity in the early hours of July 21, 2026, spotted by a tester working in a preârelease environment. A developer on X (formerly Twitter) posted that âGemini 3.6 Flash, ID âgemini-3.6-flash-tieredâ, appeared in Antigravity a few minutes ago,â confirming that the model showed up in the tool before Google announced pricing and capabilities. Another technical article describes Google Antigravity 2.0 receiving Gemini 3.6 Flash as part of a broader update, while warning that rollout was staged: some accounts saw the new model immediately, others after a delay attributed to region and account configuration. Official Google guidance later clarified that Gemini 3.6 Flash powers the default Antigravity agent in âGemini Managed Agents,â although developers can override the model setting through the API. What has Google changed under the hood compared with Gemini 3.5 Flash? Gemini 3.6 Flash mainly targets developersâ complaints about verbosity, token usage and slow workflows in 3.5 Flash. Google documentation and independent tests show lower token consumption, updated pricing and a more aggressive reasoning mode aimed at complex coding tasks. When placed side by side, the changes look like this: Token efficiency: A Google blog on Antigravity reports that 3.6 Flash consumes up to 17% fewer output tokens than 3.5 Flash on the Artificial Analysis Index, a synthetic benchmark designed to mimic real coding workflows. Pricing: Ars Technicaâs coverage of the launch notes API pricing of $1.50 per 1 million input tokens and $7.50 per 1 million output tokens , down from $9 per million output tokens in the 3.5 Flash tier. Reasoning: A technical guide explains that âthinking modeâ is enabled by default and can be given an unlimited budget, letting the model run more internal reasoning steps for hard tasks without forcing developers to manage that complexity manually. Variants: The same guide describes a â3.6 Flash Lowâ variant aimed at wellâscoped edits, test generation and singleâfile changes, with the full 3.6 Flash reserved for heavier agentic workflows. Googleâs changelog stresses that these optimizations target endâtoâend workflows, not just single responses, by reducing tool calls and iteration loops inside agents built on top of the model. How does Gemini 3.6 Flash behave inside Antigravity for developers right now? Inside Antigravity 2.0, Gemini 3.6 Flash sits at the center of Googleâs agent-first IDE. It powers code migration, refactoring and multi-user simulations while exposing configuration options to switch models or limit the agentâs reasoning budget for safety and cost control. From Google examples and thirdâparty writeâups, current Antigravity behaviors include: Code migration: Googleâs Antigravity blog shows 3.6 Flash handling legacy software modernization, moving old code to newer frameworks with lower latency and higher quality compared with 3.5 Flash. Interactive canvases: A Mandarin-language analysis describes Antigravity demos where 3.6 Flash builds interactive canvases and orchestrates SDK workflows, coordinating multiple tools and files from within the IDE. Multi-user simulations: The same source reports Google using Antigravity and 3.6 Flash to simulate several users editing an offline Markdown editor, stressing long-context coordination. Agent defaults: A Google AI Studio post states that Gemini 3.6 Flash is now the default engine for the Antigravity agent inside Gemini Managed Agents, with a specific agent version string linked to the preview configuration. Developers who want to stay on older models can still change the Antigravity model picker, but some community posts describe the 3.6 Flash rollout as a âforced upgrade for IDE holdouts,â reflecting frustration with changing defaults. How are early users reacting to Gemini 3.6 Flash in Antigravity? Feedback from Antigravity users is sharply mixed. Many welcome the faster backend and lower token bills. Others complain that the user-facing experience has regressed and that the model sometimes feels less precise than 3.5 Flash despite the architectural improvements. Public reactions collected across forums and blogs show the spread: An article on an AI-focused site calls Gemini 3.6 Flash a âblazing-fast backend beastâ but âa frontend disaster,â citing confusing UI changes and hard-to-discover configuration options in the updated Antigravity interface. In a Google developer forum thread from late July 2026, one user warns: âDonât use 3.6 Flash, it is faster but more dumb and stupid than 3.5 Flash,â complaining that code suggestions became more shallow while latency improved. The same forum discussion notes intermittent errors where Antigravity fails to run tasks with 3.6 Flash selected, prompting some users to roll back to previous models while Google patches issues. By contrast, multiple developers on X highlight smoother multi-file refactors and fewer tool calls, with one headâtoâhead demo from Antigravityâs official account showing 3.6 Flash modernizing legacy code faster than 3.5 Flash. The gap between backend metrics and frontend experience has become a core theme of early coverage. Googleâs documentation focuses on token and latency numbers, while community testers concentrate on how those changes feel inside everyday IDE workflows. What comes next for Gemini 3.6 Flash and Antigravity users? Gemini 3.6 Flash is now a general-availability model with no announced deprecation date, and Google is treating it as the standard engine for agentic coding in the near term. Developers can expect incremental updates to Antigravity and the Gemini API rather than another immediate model replacement. Signals from Google and ecosystem coverage suggest several near-term developments: Support horizon: Googleâs deprecation page lists Gemini 3.6 Flash with a launch date of July 21, 2026 and notes that no shutdown date has been set, implying multiâyear support. Rollout stability: A regional rollout explanation from a thirdâparty blog tells users that missing 3.6 Flash entries in the Antigravity model menu are likely due to staggered availability, not cancellation. Evaluation guidance: The same guide urges teams to run sideâbyâside comparisons, switching nonâcritical projects to 3.6 Flash and diffing results against existing defaults over at least a week of real work. Enterprise integration: Google states that enterprises can access 3.6 Flash through the Gemini Enterprise Agent Platform and the Gemini Enterprise app, extending Antigravity-style workflows into corporate environments. For now, Antigravity remains Googleâs main test bed for agentic coding, and Gemini 3.6 Flash is the model under scrutiny. Developers are being encouraged to measure actual workflow costs and output quality, not just headline benchmarks, before committing fully to the new default.
