CrypticAvtar

At 26, she was diagnosed with advanced rectal cancer. Four months later, there was no detectable evidence of the disease. Mrinali Dhembla had begun experiencing clear warning signs: rectal bleeding and persistent constipation. These symptoms led to more in-depth testing and a definitive diagnosis — stage 3 rectal cancer, with the disease already starting to spread beyond its original site. An underlying genetic condition was also present: Lynch syndrome, which increases the risk of developing several types of cancer. The standard approach in such cases typically involves surgery and chemotherapy. However, her medical team chose a different path. And from that point, everything changed. She was started on an immunotherapy regimen using two drugs: nivolumab and ipilimumab. The goal was to stimulate the immune system, encouraging it to recognize and attack cancer cells. The treatment lasted four months. By the end of that period, follow-up scans showed an unexpected outcome: no detectable tumor remained. Blood tests for circulating tumor DNA also came back negative. The patient was declared to have no evidence of disease. This case has drawn attention for the potential of immunotherapy in similar situations, particularly in patients with specific genetic profiles.

🦔Goldman Sachs reports that companies are blowing past their AI inference budgets by orders of magnitude, with inference costs in engineering now approaching 10% of total headcount costs and potentially reaching parity with salaries within several quarters. KPMG surveyed 2,100 senior leaders and found US companies plan to spend an average of $178 million on AI over the next 12 months, with Asia-Pacific firms budgeting $245 million and EMEA $157 million. The two reports together show companies are spending more than planned and intend to spend even more. My Take Inference costs approaching headcount parity is an extraordinary number that most finance teams did not model when they approved their AI strategies twelve months ago. The compute crunch, electrical component shortages, and GPU spot prices up 48% in two months are all flowing into corporate operating costs faster than anyone budgeted for, and Goldman's trajectory suggests it accelerates from here. What I find hard to reconcile is that $178 million average sitting alongside enterprise data showing eight in ten workers are either avoiding AI tools or not using them at all. Companies are committing to nine-figure inference budgets while their own employees aren't using what's already been deployed. I've watched this dynamic build all year and my honest read is that a significant portion of this spending is driven by competitive fear rather than demonstrated returns. Nobody wants to be the company that didn't invest in AI when everyone else did. That's how bubbles get funded, and at some point boards are going to demand a number that justifies it. Hedgie🤗

I've seen talk of AI removing the need for frameworks. As an author using AI to assist writing, it feels we aren't there. Trying to debug/fix framework code without introducing regressions is non-trivial. To think app code isn't going to be missing edge cases is fanciful.

In the next 10 years we will have what used to be programming jobs split clearly into 3 roles: - highly specialized manual coders who output 10x better quality code than any model - "normal" developers who are augmenting themselves with AI to do most of day to day software development (normal best practices apply, 99% of code is reviewed and understood by human) - and "slop wranglers" who are building everything with AI and using AI to keep slop spinning with AI review, AI monitoring, AI project management and AI ideation

In the next several years: Lots of slop shipped, huge issues with key software, feeling out of control, exciting, fun, scary, wild, stupid, hard. More agentic software dev adoption across more industries as business owners see opportunity. Devs who don’t have good boundaries will become overworked, burned out. Devs with good boundaries might be looking for new jobs as their nominal productivity doesn’t keep pace with the slop slingers. Huge security breaches exposed due to much worse quality and much better automated threats. Many of these breaches never discovered. Building software will become much more bottlenecked in other areas: product design, marketing, UI/UX, user adoption, etc. Large layoffs and reshuffling, but plenty of opportunities popping up all over. Small dev teams with a few devs and a big agent and CI budget will become more of a norm. It’ll feel more like startup times. New ways of working that are more sustainable will be developed. Better expectations around what makes a good software developer are established. Lots of disruption in nearly every industry as new players come online with competing software, much smaller and scrappier. Legal fights all over. Shifting landscape, hard to plan ahead so nobody bothers, it’s easier to just iterate as challenges pop up. Anthropic eventually learns how to be professionals. Their creativity and innovation pace slows, but they don’t constantly shoot themselves in the foot, and manage to stay relevant as a result. Google eventually releases a decent coding model that is a fraction of the cost of the others, and it takes over a good chunk of the market. Many still hate it. OpenAI becomes the market leader among devs again by default as Anthropic stumbles, and immediately people realize why they left for Claude in the first place. But they don’t go back. A slow difficult grind toward regaining software quality as business owners realize it can’t continue like that. Really hard core automated validations (specifications, tests, and more) will become everyone’s focus, because it allows for much higher quality agentic code. A sizable chunk will be agentic teams that use all good models as checks and balances for each other. Ping-ponging implementation plans back and forth between different models until all angles are considered is considered a best practice. AI and CI spend stays very high, despite lower token costs. Exploratory implementations will be normal: it’s just as easy to implement as it is to plan, so might as well have a dozen implementations of every idea to test and evaluate for inclusion. Even game devs and system devs will succumb eventually to agentic coding, except in small pockets around certain influential recalcitrant streamers. However, these streamers will help hold the quality line across the industry through sheer force of will. A slow return to software quality as a result of all this. More situations where you truly do not need to read the code; there are enough checks and balances that the average human review is less useful than the existing validations. More seamless, well-integrated agent + validation solutions to various types of software and platforms. In-house at first, and then available as dev products or open source. Self-healing software becomes fairly normal — where bugs are reported, reproduced, fixed, and eventually deployed. Eventually the average software quality will exceed 2021 levels by a large factor. 100X more software will exist in nearly all facets of life. Advancements in robotics makes us all forget about how crazy AI software is. I’ll still be working on helicopter games and driving my tractor. My grandchildren count will increase. All this before GTA 7.

ETFs made my wife and I millionaires in 7 years. But there's over ~10,000 to choose from. Here are 10 ETFs that can make you a millionaire in 2026: Total US Stock Market: $VTI This is my bread and butter. It tracks the ENTIRE US stock market... So you get a piece of every pie 😉 Best Year: +30.9% (2013) Worst Year: -38.4% (2008) 10 Year Average: ~14% Total International: $VXUS This tracks everything that $VTI doesn't. If you want global diversification without picking countries... This is your best bet. Best Year: +32.4% (2025) Worst Year: -16.1% (2022) 10 Year Average: ~10% Nasdaq 100: $QQQM This tracks the top 100 (non financial) US companies. It's heavily weighted toward tech, AI, and innovation... So it's high risk, high reward. Best Year: +56.4% (2023) Worst Year: -32.6% (2022) 10 Year Average: ~20% S&P 500: $VOO This is Wall Street's "gold standard." It tracks the 500 largest US companies... And 92% of fund managers can't beat it 🤷‍♂️ Best Year: +32.4% (2013) Worst Year: -18.2% (2022) 10 Year Average: ~15% Small Cap Value: $VBR These are smaller US companies trading at discount valuations. They're more volatile than large caps... But they have a great track record. Best Year: +36.6% (2013) Worst Year: -32.2% (2008) 10 Year Average: ~10% Growth Stocks: $VUG These are the fastest growing (large cap) companies in the US... And they're perfect for aggressive investors. Best Year: +46.8% (2023) Worst Year: -33.2% (2022) 10 Year Average: ~17% Real Estate: $VNQ This is a dividend heavy fund that tracks REITs. (Real Estate Investment Trusts) It's like being a landlord without the tenants 🤣 Best Year: +40.5% (2021) Worst Year: -37% (2008) 10 Year Average: ~6% Tech Stocks: $VGT This tracks software, hardware, & any tech you can think of. It's the highest returning ETF on this list... But if a -40% year would make you sell, skip it. Best Year: +61.9% (2009) Worst Year: -42.8% (2008) 10 Year Average: ~22% High Yield Dividend: $VYM This is the "passive income" ETF. It targets stocks with the highest dividends... So you get paid while your portfolio grows 😏 Best Year: +30.1% (2013) Worst Year: -31.9% (2008) 10 Year Average: ~12% Total World Market: $VT This lets you buy the entire world. It's split 60/40 US / International... With 9,000+ total stocks 🤯 Best Year: +32.7% (2009) Worst Year: -33.1% (2008) 10 Year Average: ~13% If you want help getting started... Hit the link in my bio 🎯

Over the past week, you may have seen an anonymous post about Delve. While we responded to it in a day, we want to provide more details about what’s true, what's not, and some changes we’ve made. There’s one question behind everything: did Delve fabricate compliance evidence or issue fraudulent audit reports? No. We did not. → Delve is an AI compliance platform that connects customers with independent auditors. We are not an auditor, just as tax preparation software is not an accountant. We have never signed an audit report. → Using default templates for our customers, just like any other compliance platform, is not “faking evidence.” These are meant to serve as a starting point for customers. → Delve does have automation in the platform, with 600+ automated integration tests, an AI Copilot to guide customers through compliance, AI code scanning, and more. -- We built Delve to accelerate innovation by bringing AI to compliance. In doing that, we pushed hard on automation. However, we now realize we didn’t provide enough clarity about what is automated, what is customer-provided, and what is independently audited. We have been working relentlessly to make improvements over the last week. -- On our auditor network: Delve connects customers with independent auditors. Some customers choose their own auditors, but many use firms in our network. Questions have been raised about some of those firms, including ones used by other platforms. Going forward we will set a higher bar in how our auditor relationships are structured and how the process is experienced by customers. Delve is rebuilding our auditor network, removing firms that don’t meet our standards, and offering complimentary re-audits and penetration tests to every customer. On platform templates for our customers: Delve provides default templates, just like many other platforms, for policies, board meetings, risk assessments, and more. These are designed to be starting points only. We should have been more explicit about how they are meant to be reviewed and customized by customers. We are making that indisputably clearer within the platform. On draft audit reports: Third-party auditors are responsible for independently reviewing all evidence and issuing final reports. We built automation that interacts closely with independent audit workflows to help expedite the process on behalf of our customers. However, this contributed to confusion about where automation ends and independent judgment begins. From now on, Delve will no longer automate these parts of the process. Furthermore, customers have a direct line of communication with their auditor to enhance transparency in any audit communications. -- We started Delve because we went through compliance ourselves and saw how slow, expensive, and manual it was. To anyone that wants to sit down and discuss our product philosophy and improvements, please reach out and let’s chat about it.