wenc · HackerTrans

wenc

9,692 karmajoined il y a 10 ans

Submissions

Using DuckDB as a Unix Tool

duckdb.org

44 pointsby wencil y a 2 ans2 comments

The McNamara fallacy: Measurement is not understanding

mcnamarafallacy.com

365 pointsby wencil y a 4 ans217 comments

Moderna CEO Noubar Afeyan on the Permission to Leap

conversationswithtyler.com

2 pointsby wencil y a 6 ans0 comments

MS Teams: New Together Mode, Dynamic Views and Other Features

microsoft.com

2 pointsby wencil y a 6 ans1 comments

Fair Division

en.wikipedia.org

1 pointsby wencil y a 7 ans0 comments

Cloudera and Hortonworks merger means Hadoop’s influence is declining

venturebeat.com

33 pointsby wencil y a 8 ans9 comments

BeakerX: Jupyter Kernels and Extensions for JVM and Spark (1.0 Released)

beakerx.com

2 pointsby wencil y a 8 ans0 comments

BeakerX: Jupyter Kernels and Extensions for JVM and Spark

beakerx.com

2 pointsby wencil y a 8 ans0 comments

Bayes and Deadweight: Using Statistics to Eject the Deadweight from Your Life

fs.blog

1 pointsby wencil y a 8 ans0 comments

White Women Tears – Critical Theory on Lindsay Shepherd

quillette.com

2 pointsby wencil y a 9 ans0 comments

comments

il y a 5 joursdiscuss

I just fed this to GPT 5.6 Sol:

  Counterexample to Jacobian conjecture:

  ((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

GPT wrote some SymPy code to check it. The response?

"As written, this is an explicit counterexample to the Jacobian conjecture. I checked it using exact symbolic algebra.

I do not see an algebraic catch in what you typed. Unless a term or exponent differs from the intended expression, it appears to disprove the conjecture. This deserves serious independent checking rather than casual dismissal."

Waiting for someone to write the Lean proof.

il y a 10 joursdiscuss

History shows that DRAM is a brutally cyclical business with boom and bust cycles. It's also heavily capital intensive. You build an expensive fab now, and then demand plummets and you're stuck selling at near cost while carrying enormous fixed assets.

IBM is already not the best at making strategic calls. I can't imagine them being saddled with DRAM business with its repeated busts.

There is graveyard of DRAM companies that never made it.

il y a 11 joursdiscuss

I still use Logseq and conceptually it’s still a great method for building a second brain. It fits the way my brain works.

But it has been dormant for years and early attempts at syncing didn’t work well. I paid to support the sync effort but we saw nothing for years. That’s a painfully long time.

il y a 20 joursdiscuss

We can disagree about Lynnwood (that's my opinion).

But Seattle does have very low social wattage.

p.s. I should clarify about Lynnwood since this is contentious for folks. For me, Lynnwood produced more social collisions that I care about than Seattle does. It had a lot of immigrant businesses, actually good restaurants, bookshops, hobby shops, etc. where people lingered, even though it's true you have to drive everywhere. It doesn't have urbanity, but it produced more lived energy than an actual urban place like Seattle did.

I also used "suburban" in two senses: physically suburban versus socially suburban. Lynnwood is physically suburban. But Seattle felt socially suburban: private, subdued and short on spontaneous public life -- at least to me (as someone who doesn't drink). An actual suburb like Lynnwood felt less socially suburban to me, with its late night cafes and things to do.

Same with Ballard. It's a fairly quiet part of Seattle, but the social collisions there were somehow better than say SLU.

il y a 20 joursdiscuss

No it is not. (lived experience as an ethnic minority). Lynnwood had way better restaurants and cafes that opened late.

Liking Ballard doesn't mean I endorse its energy. Ballard is one of the quietest parts of the city (the Nordic Museum is there), but people were also the chattiest, which is why I liked it. It provided a brief respite from the Seattle Freeze.

Ha, those writers don't live in Seattle full time. They're only visiting.

il y a 20 joursdiscuss

I've lived all over the country, both in big and small cities, and most recently in the Seattle area (across the lake in Kirkland) for 4 years.

Seattle has trappings of a city, but socially it doesn't feel like one in the way Chicago and NYC are (ok they're bigger, but hear me out -- it's not the size, it's the people). To me, Seattle feels like Cleveland but with more money.

I couldn't quite put my finger on it, but I would visit different neighborhoods from Capitol Hill to ID to Northgate to Ballard (I liked Ballard the most) almost every weekend, and everything just felt so subdued compared to a city that is truly alive. I had to take trips to Vancouver -- a similar city but more alive -- just to get my dose of city energy. Even Lynnwood WA -- a suburb -- had more energy.

The city itself has too much monoculture -- predominantly tech bros or hipsters or nature people -- but that's not enough diversity to create true energy.

The food scene was uniquely mediocre relative to its wealth and size. It had pockets of good stuff, but overall just very little risk-taking and experimentation in the restaurant industry because of the economics (min wage is $21.30 which is fair to workers but hard for small business owners) and insufficient population density to turn tables at a high rate (the land is fragmented by water and mixed elevation), and high proportion of food-as-fuel population.

Seattle attracts who it attracts because of what it is -- introverted, nature loving, affluent in a countercultural way. But this does not create a vibrant city.

Seattle's social energy resembles that of a paradoxical population who want to live in a city but are secretly suburban people.

il y a 21 joursdiscuss

I did a search and found that the satellite internet market size is $16B today and $38B in 2031 (5 years from now)

https://www.mordorintelligence.com/industry-reports/satellit...

But I also think that focusing on residential internet as the only market might be thinking too small.

There's aviation, maritime, defense, telecom/enterprise backhaul, remote industrial (oil rigs, mines, etc.), and those guys are are not paying $135/month.

This might unlock new applications, like remote sensors and autonomous devices that are out of coverage areas today.

John Deere's farming equipment for instance is already on Starlink, and those things are basically computers on wheels.

The only issue is that satellite internet needs line of sight to the sky. Underground/undersea applicatons are basically out.

le mois dernierdiscuss

As someone from Chicago (actual Chicago, south side, not the suburbs), randomly talking to strangers is what we do.

We're talking to strangers at the bus stop, at the grocery check out, or just wherever. It's just phatic conversation, nothing needs to come of it. Chicagoans aren't just friendly, they actually love the art of the conversation -- every conversation is a chance to put in the reps.

But the minute you step into the suburbs, this habit disappears.

le mois dernierdiscuss

Just to add to your Texas comment: there are a few larger states that have distinct hubs where people think so differently, that they might as well be living in different states. People who haven't lived in these states don't really have a good feel for how different the internal cultures can be.

California: we know how SoCal is culturally worlds apart from Norcal, and both are worlds apart from inland California.

North Carolina: culturally Charlotte ≠ Research Triangle ≠ Greensboro-Winston Salem-High Point. There is no single NC culture.

Florida: the stereotypes exist, but I've visited different metros in Florida and they couldn't be more different. South Florida (Miami) is very Latin while the panhandle (Tallahassee, Pensacola) couldn't be less Latin -- it's mostly southern culture. Orlando, Tampa are also way different.

Florida in fact shouldn't be governable -- every part of the state has different interests. Yet it somehow works.

le mois dernierdiscuss

Attached is the writing.md I use to steer Opus 4.8. Prompt:

  "use the writing.md steering on x.md and loop until all LLM traces are removed".

I ran it on TFA and Pangram flagged it as LLM generated but Claude Fable couldn't definitively tell.

-- writing.md ---

# Writing Rules (MANDATORY)

## Banned Words and Phrases

Never use: "incredibly", "extremely", "absolutely", "fundamentally", "dramatically", "crucial", "vital", "powerful", "robust", "elegant", "seamless", "cutting-edge", "game-changing", "groundbreaking", "It's worth noting", "Importantly,", "Interestingly,", "Let's dive in", "At its core,", "At the end of the day,".

No exclamation marks in technical writing. No contractions in formal writing.

## Banned Sentence Structures

1. *Semicolons joining independent clauses.* Do not write "X does A; Y does B." Use a comma + conjunction that names the relationship: ", while" (contrast), ", and" (addition), ", so" (consequence). Semicolons hide the logical link and sound artificially balanced. 2. *Label-colon-explanation.* Do not write "The key insight: ..." or "The limitation: ...". State the point directly or use "is that" phrasing. 3. *Colon after a bolded term.* Do not write "a *rollout engine*: a lightweight...". Use a comma. 4. *Sentence fragments as assertions.* Every claim needs a subject and verb. "No gap at any ρ." → "There is no gap at any ρ." 5. *Em-dashes joining independent clauses.* Do not write "X does A — Y does B." Use a comma + conjunction. Parenthetical em-dashes ("the policy — trained offline — cannot adapt") are fine. 6. *Tricolon lists of near-synonyms.* "It does not X, Y, or Z" is padding unless each item is genuinely distinct.

## Banned Rhythms

1. *Staccato sequences.* Two or more consecutive short declarative sentences of similar length. Join them with a conjunction or subordinate one. A single short sentence standing alone for emphasis is fine and often good. Do not eliminate it. 2. *Formulaic layout.* Do not produce: intro paragraph → three bullets → summary paragraph. 3. *Gratuitous parallelism.* Do not force list items into identical grammatical form if it makes them sound robotic. 4. *Saying it twice.* If you stated a fact, do not rephrase it from another angle in the same paragraph. One clear statement is enough. 5. *The negation-correction reversal.* This is the move where you deny one candidate and assert the real one. Surface forms to match: "not X, but Y"; "it isn't X, it's Y"; "X was never the point, Y was"; "for me X, for them Y"; the comma-tag "Y, not X" ("sanctioned, not stolen"); the "not so much X as Y" form; and the gapped version where a stranded verb delivers the pivot ("Hours aren't the bottleneck. Attention is."). One reversal at a genuine turning point is good writing. The structure is not the problem. The density is.

   Detection is a whole-document pass, not a per-paragraph glance. Read the entire piece and mark every sentence or sentence pair that negates one thing to elevate another, including the comma-tag and gapped variants above. Count the marks. More than one per ~300 words, or more than three in a short piece, means the reversal has become the default sentence engine, which is the machine tell. A single dense paragraph with two stacked reversals also counts.

   Fix by thinning, not by deleting all of them. Keep the two or three that land on the strongest turns. Rewrite the rest as plain declaratives that state the point with no foil ("The bottleneck is attention now." instead of "Hours aren't the bottleneck. Attention is."). Removing every instance flattens the voice, so the aim is to make the survivors rare enough to regain their force.

6. *Repeated hedge adverbs.* A softener like "almost", "somewhat", "rather", "a bit", or "fairly" used more than once in close range becomes a tic. Keep at most one, and only where it earns its place.

## Positive Rules

- Active voice. Use "we" and "our". - Concrete nouns and verbs. "The model overfits after 50 epochs" not "exhibits suboptimal generalization characteristics." - Plain English. Use technical terms only when they carry meaning plain English cannot. - State consequences, not meta-commentary. "The policy has no lookahead" not "training compresses multi-period consequences into a single-step mapping." - One sentence that advances to the next thought beats two sentences restating the current thought. - State assumptions when uncertain. Do not hedge-stack ("it might be the case that perhaps..."). - Not every paragraph needs a topic sentence or a concluding sentence. - Do not resolve the ending with a tidy bow. A piece may close on an open question, an admission, or an unresolved tension. Summary endings that restate the thesis read as machine-generated. - Do not over-smooth. Removing every short sentence, every parallel, and every fragment flattens prose into uniform medium-length sentences, which is itself an LLM smell. Fixing a tell should not cost the voice. - When editing existing text, match the density and register of surrounding paragraphs. - Direct and conversational register, but no contractions in formal writing. Personal essays and conversational pieces keep their contractions; the no-contraction rule applies to formal and technical writing only.

le mois dernierdiscuss

I have a steering .md file that instructs Opus how not to sound like an LLM when writing prose (I write prose in my IDE with Opus). The steering is specific to me, but I've found that giving Opus rules like eschewing punchy journalistic sentences ("Not because X. But because Y. And that matters."), varying sentence lengths and avoiding staccato sounding clauses go a long way in smoothing out LLM smells in writing (at least according to me).

Aside: different LLMs sound different too! ChatGPT is the worst offender for LLM-sounding writing and needs the most smoothing, but Claude (web) actually sounds like a humanities major from the get-go.

il y a 2 moisdiscuss

I can attest to this. I work in Midtown Manhattan. You'd think walking around meant getting distracted by the all the activity around you that you'd forget about the problem you're trying to solve.

But I've found that distraction is the catalyst. Creativity for me comes when I focus on something else for a while, not grinding on the same problem with unwavering focus.

il y a 2 moisdiscuss

People often say Mandarin and Cantonese are like Spanish and Portuguese, but that undersells how different they are.

Your example of Spanish and French is more accurate -- same language family, but different grammar and vocabulary.

I offer German and Dutch as another example pair -- same language family as well, but different enough that no one will say "oh they're just different dialects". Dutch is an example of what happens when a Germanic language (Low Franconian) gets it's own state.

il y a 2 moisdiscuss

Category theory is rarely useful by itself, but it can be a mental scaffold when designing things like query languages. Microsoft's LINQ dsl within C# used category theory ideas to ensure consistency. That said, the applicability surface area in practice is typically quite limited in my experience. It's like formal methods -- elegant in practice, but a good problem fit is often rare. It's like writing a LEAN proof for your web app -- rarely needed, but if your web app needs a high degree of correctness, then indispensable.

This is John D Cook's take:

Category theory can be very useful, but you don’t use it the same way you use other kinds of math. You can apply optimization theory, for example, by noticing that a problem has a certain form, and therefore a certain algorithm will converge to a solution. Applications of category theory are usually more subtle. You’re not likely to quote some theorem from category theory that finishes off a problem the way the selecting an optimization algorithm does.

I had been skeptical of applications of category theory, and to some extent I still am. Many reported applications of category theory aren’t that applied, and they’re not so much applications as post hoc glosses. At the same time, I’ve seen real applications of categories, such as the design of LINQ mentioned above. I’ve been a part of projects where we used category theory to guide mathematical modeling and software development. Category theory can spot inconsistencies and errors similar to the way dimensional analysis does in engineering, or type checking in software development. It can help you ask the right questions. It can guide you to including the right things, and leaving the right things out. [1]

[1] https://www.johndcook.com/blog/applied-category-theory/

il y a 2 moisdiscuss

If you lived in a high place (Denver), you will find it different from a flat lowland (Chicago).

Also in Rio, how high you live can be a marker depending on which part of town you are. Favelas are on hills, whereas wealthy people in Zona Sul live down the hill closer to the beaches.

il y a 2 moisdiscuss

But why though? DuckDB can still be used as a local query engine — I still use it as that. I haven’t touched any of the DuckLake stuff and the duckdb cli and Python library are still my bread and butter. They can add new use cases, but it doesn’t affect the core engine.

Is the concern that the duckdb messaging is now diluted by it having all these extra features? That you can’t sell it to friends as “this thing” like you can a one use tool like curl? I get that, but I also feel that duckdb is so much bigger than a “do one thing and do it well” tool.

It’s an engine that drives the modern data tool stack. Duckdb’s team has been prescient in that it has made many tasteful bets on what users want —- the ability to interop with pandas and polars, addition of geospatial, the plug-in infra. They’re all optional but when you neeed these things, they’re so useful. They’ve also clued me into what the broader data world is thinking about (I didn’t know about sketches and hilbert, but those are so useful in probailistic large scale queries and in geospatial queries). And they exist in larger database systems like Redshift too.

So far duckdb’s bets have been tasteful, and mostly ignorable if you don’t happen to use them.

il y a 2 moisdiscuss

DuckDB is both a standalone and a component. This effort is actually very coherent and brings it back into a familiar usage model — that of a traditional client server RDBMS.

RDBMS have always been multi-user concurrent systems. DuckDB is a very fast local engine that has a multitude of use cases because it is a embeddable in other systems.

It’s like saying what does SQLite wanna be? It’s in your phones, your browser, your desktop apps, iot devices and people have extended it in different directions. The only difference here is that this is first party not third party. But to me it’s a very legible move.

il y a 2 moisdiscuss

When did FT become Business Insider?

I have an FT subscription and they keep moving toward this kind of narrative first reporting to get clicks. It’s no longer a believable paper.

il y a 3 moisdiscuss

It sounds like this replaces the PCA reconstruction function with a quadratic.

The normal PCA encoding:

1) Given a mean-center-scaled X matrix, get the latent variable matrix T with X = T * P’ + e, where P = loadings and e = residuals. The P is your model, so for a new vector xnew, you can calculate tnew = xnew * P (because P’ * P = I).

This is the encoder —- nothing changes here. The original matrix is dimensionally reduced with residuals e discarded. This is why PCA is lossy.

The decoder is where things diverge

The usual PCA decoder reconstructs a given latent variable t_any by using the trained P loadings, like thus x_reconstructed = t_any * P’. This reconstructed data lies on a linear hyperplane, so if the original data did not lie on the hyperplane, reconstruction errors are potetially high.

In your proposal, instead of a linear decoder, you train a quadratic decoder (essentially a classic ridge regression using a quadratic) on the original X. So for your reconstruction, you have x_reconstructed = poly(t_new).

This achieves lower reconstruction error in-sample (naturally, because quadratic is higher order than linear), but your poly function is trained on a particular corpus. Which means that when you’re in-distribution within that corpus, you’re good but when you’re not, you can be very wrong in biased ways that PCA’s linear reconstruction is not.

SO this is not a better technique than PCA in a general sense. It’s a better reconstruction machine when your data is mostly in-sample. It’s a kind of computationally cheap “specialization” on a particular distribution of data, which can be useful if you’re mostly in-distribution but introduces new risks when out-of-distribution.

Whereas PCA just drops the residual and makes modest claims, a quadratic decoder is trying to predict the residual and on out-of-sample data, it can be wrong in biased ways that PCA is not. In other words, it can hallucinate.

But if on a large enough training corpus, chances are we’re going to be in-distribution most of the time, so maybe this could generalize well.

il y a 3 moisdiscuss

Right now Alexa+ and Gemini are objectively better.

The best is ChatGPT voice mode. It understands non English words and accents amazingly well, and even though the LLM model isn’t the full fledged one, I can have deep conversations with it for an hour without it missing a beat.