Jeffrey Scholz ✓
@Jeyffre · Jakarta, Indonesia · joined 19 Sep 2010
Building the graduate school of blockchain engineering @rareskills_io Passively open to new job opportunities?: https://t.co/xRu6uLmyrF
15 885Followers
914Following
5 951Posts total
13.3MViews on collected posts
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I think a lot of software engineers felt this way in December of 2025 when Claude code could actually run unsupervised for an hour.
Lo and behold, software engineers are still here 9 months later, and the job market is still improving.
Now, I understand math is not quite the
18.9K views · 130 likes
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Learning is primarily a social activity.
This is why, even though classrooms are not effective, they have a far higher completion rate than self-paced moocs.
This isn't unique to learning:
- gym = build cardio/muscles with buddies
- office = work on a project with coworkers
-
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You are so gleeful about my life as a mathematician crumbling. Why? Did I do something to you? Did I ever do something to you beyond the crime of learning and thinking about things?
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Explaining a subject in your own words is the most efficient way to learn a subject, and it continues to work even as you get really advanced.
It forces a durable encoding of the subject in your own mind (you memorize it better) and it exposes your knowledge gaps.
Explaining in
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Take 4 minutes out of your day and watch this piece of art.
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Since I just gained a large(r) following after the quantum computing thread, I should re-introduce myself.
I'm the founder of @RareSkills_io. It's a free education platform for web3/blockchain programmers and smart contract auditors (cybersecurity for web3).
RareSkills is most
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Ok, this thread took off.
For those who are new followers, I’m the founder of the company @RareSkills_io
We focus on making difficult subjects in computer science, particularly blockchain, approachable and useful to you.
Our most famous resource is the “RareSkills ZK Book”
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@Jeyffre In summary there is an improvement in Quantum computing but not close to the stage where it frightens the security of Bitcoin and also it is some kind of PR buzz by Google to put it out there that we are up to something. Gotcha!
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In my opinion, this whole thing is a regular exercise in technological marketing
1. Write about some seemingly powerful tech that creates fear (AI, nuclear, quantum, etc). Fear creates engagement.
2. Do a PR campaign where journalists write clickbaity headlines on a subject
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Quantum computing is kind of at the stage right now where some smart teenager wired a few logic gates together in a random fashion and said "hey look, my circuit made a random output and didn't explode!" Compared to previous attempts, it is an improvement. But he is still a long
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But how big a step is this towards quantum computers breaking cryptography?
What google did is create a circuit with 9 gates, 25 gates, and 49 gates (very roughly speaking) and noticed that the accuracy of the output seemed to improve — or rather the computation failed less.
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However, note that the problem is "rigged" in favor of quantum computers. The benchmark is explicitly modeling a quantum phenomenon, so *of course* we get a speedup.
In other words, Google created a random distribution on the output that "seems correct." Why does it "seem
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By analogy, this is what Google was doing. The computation Google was doing was a "pseudo-random quantum circuit" (think pseudoranom ice cube) but we know a quantum circuit is just matrix multiplications (on crack). Therefore, it is a bunch of random matrix multiplications with
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However, you can *estimate* the distribution of how the laser will scatter without a quantum computer, so you can have at least a rough idea if your answer might be correct.
Theoretically, a quantum computer can predict what the laser will do when it enters and exits the ice
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Let's look at a specific (oversimplified but helpful) example. Suppose you shine a laser beam into an ice cube.
Actually simulating what the laser will do when it exits the ice cube is very hard to predict because some quantum phenomena is involved.
To actually compute what
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The current problem with quantum computers is that as the circuit gets bigger, they become less correct on average. All of the "talking to each other" creates so much noise the system stops working.
Once your probability of being correct drops below a certain threshold your
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So basically, quantum computing is "matrix multiplication of real numbers on crack."
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Like a regular computer, a quantum computer keeps bits in groups. So a 64 bit quantum computer would have a vector of 64 2d vectors serving as it's "word."
Here is where the speedup happens: in a regular computer, each of the 64 bits don't know anything about the value of any of
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Running a quantum circuit means you plug in a quantum vector, run it through a bunch of matrix multiplications, then collapse the output.
The final vector will be the correct answer. Technically, quantum computers can give wrong answers, but if you run the computation multiple
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For example, a non-collapsed qbit might look like [0.8366, 0.5477].
A quantum gate is effectively matrix multiplication. If you multiply a qbit (2-dim vector) by a 2x2 matrix, you get another qbit (2-bit vector) back. For example, a quantum NOT gate looks like the 2x2 matrix htt
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A quantum computer, instead of using bits, uses a qbit.
A qbit is a two-dimensional vector of real numbers. This means one qbit can hold "infinitely" more information than a single regular bit.
A bit can only hold 0 or 1, but a qbit can hold a 2d vector of real numbers, so it
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You can think of an individual qbit as a "probability distribution" -- it averages around certain values but doesn't have a concrete value.
However, when the qbit is "observed" it is forced into a vector representation of either [1, 0] represnting a binary 0, or [0, 1]
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First we need a crash course in quantum computing.
A classical computer keeps information in 64-bit groups called "words."
Each of these groups of bits are run through logic gates such as AND, OR, and NOT.
These gates take one or two bits as an input and return a single bit as
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I read Google's paper about their quantum computer so you don't have to.
They claim to have ran a quantum computation in 5 minutes that would take a normal computer 10^25 years.
But what was that computation? Does it live up to the hype?
I will break it down.🧵
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Against accounts of the same size
5 posts from the last 90 days, next to the 10K–100K follower range. shown to more people than peers of the same size.
Median views4 979this account924median for 10K–100K
Reach, %31.34%this account3.62%median for 10K–100K
Engagement, %1.57%this account1.52%median for 10K–100K
| Metric | This account | Median for 10K–100K | Ratio |
|---|---|---|---|
| Median views per post | 4 979 | 924 | 5.39× |
| Reach (views ÷ followers) | 31.34% | 3.62% | 8.66× |
| Engagement rate | 1.57% | 1.52% | 1.03× |
Others in this range → Compare with another account → How these benchmarks are built →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
396.1K11 Dec
374.4K
369.2K
344.4K
326K
350.4K
29.4K
243.5K
101.6K12 Dec
5K15 Sep
2.7K22 Sep
152.5K
3.2K23 Sep
18.9K24 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.21%11 Dec
0.29%
0.40%
0.27%
0.43%
0.96%
1.44%
0.72%
0.44%12 Dec
0.48%15 Sep
2.82%22 Sep
1.57%
3.47%23 Sep
0.79%24 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes62.8%48 290 in total
Reposts4.8%3 698 in total
Replies1.3%962 in total
Quotes0.8%603 in total
Bookmarks30.4%23 365 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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