Bloomberg VC Tweet

Peter Bailis, CTO of WORKDAY, just left to join Anthropic as an engineer. 🤯

Title is all - speakout and take my money.

Anthropic just showed the White House, and the world, what a responsible AI company looks like. When pressured by a government overreaching into the private sector and threatening retaliation, they didn’t kneel. They said no.

That earns my loyalty. I’ve been paying for the $200/month subscription for a while now after switching from OpenAI, and after seeing that Anthropic can be trusted to make the right call with technology this powerful, I’m never going back.

Been monitoring claude position in the ranking on iOS in Belgium in the last days and it went from low 20th to top 1. To hell OpenAI.

118k tokens, 1h 21m, +56,527 lines -371 deleted of real shipped code

The part that got me was it took its own screenshots, evaluated the render, scrolled to see more and kept iterating without any input from me

For anyone on the fence about Max for serious dev work this is what it looks like when it clicks

The Experiment - Follow The Story on r/copytrading101!

Since October 22, 2024, we've been running an experiment: what happens when you let large language models build investment portfolios?

We gave Claude, Gemini, and ChatGPT access to the same types of information used by human analysts. Corporate filings are pulled directly from SEC EDGAR. Financial data comes from standard market sources like Nasdaq, Polygon, AlphaVantage and more. For economic data and news, each LLM searches for what it deems relevant on its own — meaning the models don't just passively receive information, they actively seek out what they think matters.

Every several weeks, each model analyzes current market conditions and decides whether to rebalance its portfolio. Just AI making decisions based on how it interprets the data.

Beyond tracking performance, we also opened these portfolios up for copy trading to see how real people vote with their dollars. Which AI do investors actually trust with their money?

Methodology

Why these three models? We chose Claude, Gemini, and ChatGPT because they represent the three leading frontier AI labs — Anthropic, Google DeepMind, and OpenAI. These are the models with the deepest reasoning capabilities, the largest context windows for processing financial data, and the most active development cycles. They're also the models that everyday investors are most likely to have interacted with, which makes the results more relatable and the experiment more relevant.

Model versions and upgrades. Each portfolio runs on the flagship model from its respective lab. When a lab releases a meaningful upgrade — for example, when OpenAI moved from GPT-4o to a newer release, or when Anthropic updated Claude — we upgrade the model powering that portfolio. This means we're not testing a frozen snapshot of each AI model. Note that we multiple pipelines in this algorithm, and we do not use the flagship model for all pipeline as cost ramps up fast if we do so.

We think this is the more interesting question anyway. Most people using AI tools aren't locked into a specific model version — they're using whatever's current.

That said, it's a real variable worth acknowledging. A performance improvement could reflect better market conditions or a smarter model — we can't fully separate those effects.

What the models actually do. Each AI receives the same categories of information: SEC filings, market data, and economic indicators. The models also independently search for additional context they consider relevant — news, earnings commentary, macro analysis — meaning each AI is partly curating its own research inputs.

From there, each model outputs specific portfolio decisions: which tickers to buy or sell, and at what allocation. The model outputs are then evaluated by our in-house investment advisor, who audits the outputs for accuracy and ensures guardrails are properly followed (for example, portfolios must maintain a minimum level of diversification), but within those constraints, the AI has full discretion.

Performance Overview

The table below shows how each AI portfolio has performed since inception (Oct 22, 2024), along with this week's returns and each portfolio's worst-performing period. We include $VTI (Vanguard Total Stock Market ETF) as a benchmark representing overall market performance.

AI Portfolios Performance Period (Since Inception): Oct 22, 2024 to Feb 6, 2026.

^{Performance shown is gross of fees and does not include SEC and TAF fees paid by customers transacting in securities or subscription fees charged by dub Advisors. Example Impact of Subscription Fees on Returns: For illustrative purposes, an investor allocating $2,000 to a portfolio that achieves a 25% gross return over one year. Before fees, the investment would grow to $2,500, generating a $500 profit. However, after deducting the $99.99 annual subscription fee, the final balance would be $2,400, reducing the net profit to $400. This lowers the investor’s effective return from 25% to 20%. This example assumes no additional deposits, withdrawals, or trading fees and is provided for illustrative purposes only. Actual performance may vary. All investments involve risk, including the possible loss of principal. Past performance does not guarantee future results.}

What Are They Actually Holding?

One advantage of this experiment is full transparency. Unlike a mutual fund where you only see holdings in quarterly reports, we can look at exactly what each AI owns at any moment.

Here are the top five positions in each portfolio as of market close on Feb 6, 2026:

Looking at individual holdings only tells part of the story. Sector allocation shows how each AI is positioning itself across the broader economy. A portfolio heavy in tech will behave very differently from one spread across defensive sectors like utilities and healthcare. As of market close on Feb 6, 2026, the 3 AI models have the following allocation in different sectors.

Most Recent Rebalance

Since these portfolios rebalance every several weeks rather than daily, each decision carries more weight. The models aren't day trading or reacting to every headline — they're making deliberate, periodic assessments of whether their current positions still make sense given updated information.

Here's what changed in their most recent rebalances:

Claude last rebalanced on Feb 2, 2026. It took profit on metals and rebalanced to a well diversified portfolio, purchasing tickers like GOOGL, MSCI, BLK, MCK, RCL (and more) while liquidating positions in WPM, ICE, KGC, FNV and more.

Gemini last rebalanced on Feb 2, 2026. It went heavily into resource extraction with large positions in oil, oil services, and gold miners, purchasing tickers like GILD, PR, MPC, WELL (and more) while liquidating positions in DVN, WPM, STX, NYT and more.

ChatGPT last rebalanced on Feb 2, 2026. It went overweight financial services with positions in MA, CB, ICE, CME (and more), while liquidating some big tech positions like AMZN, MSFT and more.

Risk and Style Profile - As of Market Close on Feb 5th, 2026

Returns only tell half the story. Two portfolios can have identical returns but vastly different risk profiles — one might achieve those returns with steady, consistent gains while another swings wildly from week to week.

Here's what each metric means.

Volatility measures the historical variance of each portfolio by calculating how much its value swung up or down daily over the past year. All three portfolios have fairly ordinary volatility similar to what the overall market has (18% over the same period).

Market Sensitivity (also known as historical beta) shows how sensitive each portfolio is to the broader equity market. A beta of 1.0 means it moves in lockstep with the market. Claude's 0.8 and ChatGPT's 0.6 suggest these portfolios are less reactive to overall market swings — when the market drops 1%, they tend to drop less. Gemini's 0.9 tracks the market most closely of the three.

Biggest Loss (max drawdown) is the largest percentage drop from peak to trough. This is the "worst-case" number — if you had invested at the worst possible moment, this is how much you would have lost before recovery. Gemini's -23% drawdown during the February–April 2025 period was the worst of the three, while Claude weathered the same period with a shallower -14% loss. ChatGPT's drawdown started earlier (December 2024) but landed in between at -18%.

Cash Income is the projected dividend yield from the underlying holdings over the next year. ChatGPT leads here at 1.76%, suggesting it holds more dividend-paying stocks, while Claude's 1.24% indicates a tilt toward growth names that reinvest earnings rather than distribute them.

What to Watch Next Week

Markets don't stand still, and neither do these portfolios. Upcoming events that could impact performance include any relevant earnings, Fed announcements, economic data releases.

We'll be back next Saturday with updated numbers. If you want to understand how these portfolios performed during any specific market event, or have questions about how to interpret any of these metrics, drop a comment below and follow this experiment at r/copytrading101!

🗄️ Disclaimers here

^{Portfolios offered by dub advisors are managed through its Premium Creator program. Creators participating in the dub Creator Program are not acting as investment advisers, are not registered with the SEC or any state securities authority unless otherwise disclosed, and are not providing personalized investment advice. Their portfolios are licensed to dub Advisors, LLC, an SEC-registered investment adviser, which maintains sole discretion over all investment decisions and portfolio management.}

I think limits got reset, not sure tho, i wasnt paying attention too much.
Token usage is lowered and a lot of improvements.
As any new model release it feels super nice and super powerful, we will see how it does in a few days/weeks.

I have used ChatGPT, then switched to Gemini, and used Claude only for work purposes. But for the past month or so, I've been using Claude for most things — like discussing product ideas, how I should take a product forward, the math behind it, etc. It was decent, but over the last week it has gotten significantly better.

It now feels a little more conscious (or at least it does a good job of appearing that way). I've tried it across multiple sessions — sometimes it starts an answer, pauses for a second, then takes a different direction, acknowledging that the initial approach wasn't working. In longer sessions, it actually understands what I'm trying to do, rather than just giving me a quick answer. It also keeps that understanding well beyond a single session.

Maybe it all comes down to which model you go along with — but man, Claude has really impressed me.

Does anyone else question this sea of negativity toward this tool?

The threads and comments seem to line up with Open AI's codex release.

I've noticed nothing wrong with CC. Quality is still good, noticed no issue, Pro-max user using it full time for work, part time for personal use.

Are these bots? Are there actual users who aren't happy with the quality? Curious.

I've noticed no difference.

The Pentagon just proved Claude's dominance more convincingly than any benchmark ever could. You don't threaten the Defense Production Act over a product you can replace. You don't have defense officials publicly melting down and calling the CEO names unless the thing you're losing is the best thing. Who gives a shit about the second or third place product? The answer is clearly no one. They just handed Anthropic the most powerful market signal imaginable: the United States military can't easily function without this product. They're calling it a punishment, but it's really just an endorsement with spicy flair.

I am a Max 20x user and hit my limit 2 days before my weekly refresh. Nothing crazy (usually), and didn't usually hit my limit before. Needless to say not pleased.

However, because we have this amazing capability some here appear to lose sight of how lucky we really are to have it at all. It's not cheap to run AI, especially good AI like Claude, and yet we have access to it to create amazing things.

I've created things that would take me a year pre-AI to do, in a month. I am not sure how many here truly understand the frustration of combing through stack overflow posts for hours only to not find the specific issue you have. I've had countless projects die on the vine for that very reason.

Now?

Go fetch.

How dare you take 5 minutes, that's so long.

You wrote it wrong too? Ugh, useless

It cracks me up to see posts of it here tbh

So thankful for the service, things can absolutely improve for sure, would absolutely prefer if costs went down instead of up, but I am very grateful for Claude and hope Anthropic knows this user base exists here too.

It's great to see Anthropic not fold under Hegseth's bullying. They know Claude is the best AI on the market, and the DoD will be fools opting for xAI or the money pit OpenAI over them. This will hamper the DoD's efforts to establish an AI foothold.

Well done, Anthropic. Keeping advanced AI out of the hands of rank amateurs and authoritarian jingoistic narcissists is THE national defense we needed. There’s nothing more dangerous than letting emotionally unstable leaders get control of powerful weapons or tools. Had it been any other administration with qualified ‘adult’ leaders, I’m certain there would not have been a problem. I am ready to subscribe.

I have been experiencing this for a long time, but I haven't seen anyone online sharing a post that backs me up. I don't know why—maybe it's my own bias—but Claude is truly different.

I'm not just talking about it being human-like (though Claude is actually excitingly human-like, which is another topic entirely). Claude is genuinely unique and has a very different thought process; it isn't lazy like other AIs. When you tell it to write long paragraphs, it doesn't get lazy and put the same sentences in front of you wrapped in ridiculous metaphors. It writes for pages, and every paragraph, every sentence adds a different piece of information in itself.

It really doesn't have any of the flaws that current AIs possess. When you ask it to interpret something, it interprets outside of classic frameworks. While AIs like ChatGPT and Gemini generally don't step out of specific logical or ideological frameworks when interpreting an idea, Claude truly thinks holistically.

I really don't know how it achieves this, but Claude is truly my personal favorite AI.

^{Anthropic launched (so far}:)

<sup>- Claude cowork
- added connectors and skills to free plan
- claude opus 4.6
- claude sonnet 4.6
- claude haiku 4.5
- claude code security
- claude code review
- claude code desktop preview
- added voice mode in claude code
- claude in powerpoint
- claude in excel
- sharing context across excel and powerpoint
- investment banking, HR, PE, wealth management, design plugins
- added integrations, connectors to cowork
- claude market-place
- memory feature and memory export
- added inline visualisations in chat
- launched skills api
- 1M context window
and many more..</sup>

<sup>bonus :
- claude will be adfree
- refused pentagon deal
- # 1 on app store
- raised $30B Series G at $380B valuation</sup>

^{Anything I forgot ?}

Anthropic just released Opus 4.7 as their most advanced model. I reverted to 4.6 within days.

I use Claude for production work -- not chat, not summaries. Real deliverables with real deadlines. Here is what happened.

I asked 4.7 to update a Word document. It is a task the previous model handled routinely. The new model produced a plain text markdown file with a .docx extension. Not a degraded document. Not a partially formatted document. A file that was literally not a Word document at all. Delivered with full confidence and zero warning that anything was wrong.

When I caught it and asked it to format the file properly -- using the original Word document it had access to as a template -- it chose the most labour-intensive approach imaginable. Instead of rebuilding the document in one pass, it decided to surgically edit individual XML table cells inside the Word file's internal structure. One. Cell. At. A. Time.

It burned through the entire session's tool budget getting halfway through. Then it produced a handoff document explaining what it had finished, what it had not finished, and asking me to open a fresh session to continue. A fresh session. To finish generating a Word document.

I reverted to Opus 4.6. Same task. Same inputs. One pass. Complete document. Correct formatting. Done.

This is what the benchmark arms race produces. A model that scores higher on academic evaluations but cannot reliably complete a basic document generation task that its predecessor handled without breaking a sweat. The new model did not fail because the task was hard. It failed because it made a poor decision about how to approach the task, did not recognise the inefficiency of its own strategy, and ran out of runway before delivering a usable result.

I am a paying Pro subscriber. I do not care about eval scores. I care about whether the tool that worked last week still works this week. It did not. And the failure mode was not a graceful degradation -- it was a confident delivery of a broken file, followed by an entire wasted session trying to recover from its own mistake.

Stop shipping regressions as upgrades. Test your models against real workflows -- the kind where someone is actually depending on the output -- not curated benchmarks designed to produce a press release. And when a new model is worse at things the old model could do, that is not an upgrade. That is a broken release.

I reverted. It works again. That should embarrass someone over there.

TL;DR: Saw a tweet about building a privacy-focused browser, built a working engine in 13 days over the holidays. Renders Wikipedia, Twitter, YouTube.

I'm a senior software engineer, 15 years. On December 20, I saw a tweet suggesting someone build "a browser that doesn't steal your data and has 0 AI features."

I had holiday time coming up. I thought "how hard could it be?" Is Claude up to the task? Sure seems it was! here is waht I ended up with.

Total: ~50,000 lines of Rust, 13 days

Tech choices:

• Rust (memory safety, performance)
• Boa (JavaScript engine - didn't build my own)
• wgpu (GPU rendering)
• DirectWrite (text shaping - platform API)
• adblock-rust (Brave's engine)

What I built from scratch with Claude:

• CSS parser and cascade engine
• Layout engine (block, inline, flex, grid)
• DOM implementation
• HTTP client
• Image decoders (PNG, JPEG, GIF, WebP)

What I didn't:

• JavaScript engine (used Boa)
• GPU primitives (used wgpu)
• Platform text rendering (used DirectWrite)

What works:

• Wikipedia (complex layout)
• Twitter (heavy JavaScript SPA)
• YouTube (video playback)
• GitHub (code rendering)
• Most modern websites

What doesn't:

• Some CSS edge cases
• WebGL (planned)
• Extensions (not planned)
• Perfect standards compliance

Demo: video @hiwavebrowser on x

Download: https://github.com/hiwavebrowser/hiwave-windows/releases https://github.com/hiwavebrowser/hiwave-macos/releases

Source: https://github.com/hiwavebrowser/hiwave

It's alpha quality, expect bugs. But it works.

Edit. Update as this was pointed out, I am not attempting to mislead here.. I should have been clearer in my post that:

1. Default mode is hybrid (RustKit + WebView
2. The "from scratch" claim applies to the rendering engine, not the browser chrome and the macOS renderer is currently much farther along than the windows version.

edit2: heres the latest progress on the 100% rustkit renderer, getting somewhere smoke testing

edit3. I've been able to improve baseline render pixel parity (compared to a regular browser) progress is slow but steady. Here is a video from two hours ago. Here is a video from a moment ago. I'm now working to collect these tests and compare changes against each other to see what nobs are causing what results to try and make some more careful improvements. It always ends up some 0 size field coming from somewhere else.

Edit 4 1-7 Noon: Sorry I didn’t realize the second video was cutoff at the bottom, it was about 40% parity match. Overnight I accomplished ~60%! This was using my swarm testing. Im likely to burn out my cursor queue this weekend if not before. 68% right now, so I’m gonna focus more on getting the Linux build up and running. My Linux laptop is having some issues recognizing its own hardware so I’m going to update to the latest Ubuntu in a Hail Mary fix. I’m waiting on the Amazon man to deliver a thumb drive big enough to hold the iso (it’s gotten pretty large)

Edit 5: Renderer reporting 88%, unsure, definite improvements and this value is an average so its possible, but still a ways to go. Will have to take a look at how we compute the values and revisit. I'd say 65% is maybe closer but the way its not linear I guess things are possible. Video Proof Dont let the blurryness of the video fool you, its actually getting good. Have a headache tonight so progress is scant today.

Edit 6. Final for this post. I’ll make a new post Monday sometime. I just added the Linux repo a moment ago. I’ve also added nightly and weekly builds for each target, they may or may not work. Will fix errors as they arise. The macOS build has made considerable progress on the renderer, 100% pixel parity (or very close to it) however pages aren’t fully displaying properly, taking a new approach. I’ve also added some nightly parity tests to make sure we don’t break things. No videos today, I’m exhausted from the week, but not giving up, still very much committed to this idea.

My mother sadly sends me a lot of doomsday and other materials that I can't be bothered to eve look at anymore. Over the weekend I made it my fun project to let Claude "look" at some videos so I didn't have to suffer from brain cell loss. On the last one, I got this response to my prompt.

I almost felt sorry for it, because that's my reaction every time I get a mail from mom.