We're 100X'ing investigations with the first GPU visual graph AI platform and now Louie.AI, the genAI-first rethink of analyst notebooks. We enjoy working with data teams all the way from from tech companies and startups to scientists and government agencies, and on problems from threat hunting, fraud, & misinformation to supply chain, user journey, and genomics. Our partners include Amazon, Nvidia, and more.
* Louie.ai: Louie connects to your data systems so analysts can use natural language to ask questions and get back data, analyses, AI models, interactive visualizations, and everything else the Graphistry platform can do
* Graph visual analytics: Gartner measured graph as a Top 5 growing data technology for the next 5 years and awarded us as the 2021 Cool Vendor in Graph
* End-to-end GPU computing: Apache Arrow & RAPIDS.ai were both voted top data projects of 2020 & 2021
* Point-and-click workflow automation: Process automation is the fastest growing enterprise industry in history
* Graph AI (GNNs) to make it easy for operational teams to take the next step of their graph journey by automating their graph insights for tasks like detection: Winner of the 2022 US Cyber Command AI alert data challenge!
... And we're hiring! LLM app/backend engineering, AI (cyber, GNNs, ...), sales engineering, platform engineering, UI, and more: https://www.graphistry.com/careers
Submissions
GFQL: Run Cypher on Polars, CPU and GPU, no database required
graphistry.com
6 points·by lmeyerov··0 comments
18F has been laid off
bsky.app
34 points·by lmeyerov··2 comments
[untitled]
1 points·by lmeyerov··0 comments
Measuring the popularity of different vector databases
gradientflow.com
63 points·by lmeyerov··18 comments
Import PyGraphistry: 100X faster Python imports with lazy loading and Tuna
graphistry.com
3 points·by lmeyerov··0 comments
The Sharing Paradox: To scale collaborations, why we're locking things down
graphistry.com
1 points·by lmeyerov··0 comments
The Data Ride Alongs
twitter.com
1 points·by lmeyerov··0 comments
Show HN: A real-time crowdsourcing game that improves what you're writing
proofist.com
6 points·by lmeyerov··3 comments
Show HN: Proofville, A Competitive Editing Game – Crowdsource Your Proofreading
The paper's line of reasoning seems to continue the endless subjective loop of assuming your viz framework has the right abstractions & defaults , which the next person will rightfully disagree with for their slightly different eval set
We found in practice:
- LLM's generate charts fine
- LLM's tweak charts fine
- LLM's take user feedback to tweak them fine
In that sense, going higher-level for abstractions, as is being argued for here, is strictly worse: it's better to give controls so the LLM can go deep and customize
In practice, we found the choice of json config language X vs json config language Y to be pretty equivalent across different charting systems (vega, plotly, perspective, etc), LLM's do them all fine
The harder parts were deciding what a good chart is (model, reasoning, context), and opposite of this approach, giving lower-level facility for doing user change requests on tweaks, interactivity, and tricky in practice, when they have a lot of data on it.
This feels like a bit of a semantic debate, but maybe a few useful perspectives, as interpret current bespoke work as not so rosy wrt collapse:
- Synthetic datasets are typically human-steered today, which points to model collapse wrt learning from the internet. (Edit: or even simpler, model x data tapped out for cost/benefit even before collapse.) I don't think standard practice is (yet) AI looking at the internet and deciding to build its own gyms to go further. When it does, model collapse may happen again, and be even more expensive
- Distillation attacks are getting interesting here. There seem to be 2 kinds: intentionally querying other models, and maybe not so intentionally, learning from reasoning traces going through shared routers, esp. coding ones
- A lot of neolabs are trying to go where the big labs might not look as directly to avoid being squashed, which suggests they aren't ready to bet on being smarter, and that means the general AI is more about collapse / $
It's the same problem that AI faces of Model Collapse: AIs that train on the internet ultimately just end up training on one another, stop moving forward, and end up as identical polished versions of one another
I now think of it as a Dr. Jekyll/ Mr. Hyde situation for software projects:
- Dr. Jekyll: For makers, the only limit is your imagination, architectural guidance, and token budget. Time to build!
- Mr. Hyde: For projects to get off the treadmill of having to copy others to maintain you position, you need to redefine how the project works and provides unique value. Features and quality are no longer the answer. Time to fight!
AOP is a big influence for how we are designing hooks, including custom ones, for louie.ai's agent harness. More principled structure to what is already expected.
I'm unclear on AOP in general, esp as proposed here. That's a bigger leap...
We almost went with Om for our seed round, and he remains on my list of "one of the good ones". It's rare to meet folks where that becomes so apparent so quick.
curious how folks like to measure this stuff wrt load testing?
We are actively revisiting our traffic simulation approach, and a surprisingly non-obvious part has been which charts to focus on. Our case is a gpu-server-backed interactive analytics app, like a photoshop for data, so we do focus both on latency and errors, and especially around handling bursty sessions as discussed in the article.
Multiple projects are coming to the same point it seems. Motherduck has been marketing "dives" since the beginning of the year (https://motherduck.com/blog/duck-dive-and-answer/) and in the Louie.ai team, we have been iterating on different patterns for similar needs. I'm getting the feeling that the answer to SaaS apps as fixed UIs over databases being dead because of coding agents means just the fixed dashboard pattern is dead, not SaaS, and BYO UI is part of the new table stakes.
I'm curious where the pattern will go. My sense is there is a split between cathedrals vs bazaar for approach here, where cathedrals are quite rigid app builders, think framer/wix, while bazaars focus a layer below for more flexibility but less integrated.
Any thoughts on layering on-GPU work stealing or cudf on top?
For gfql (graph query language mapping down to cudf calls), we're trying to jettison the hot loop of python->cpu->gpu, so been loosely watching cuTile evolve!
This is a funny one because it seems less into what fable is being clever on and more about the bitter lesson and data flywheels
Our UX agentic engineering flow, as many others, is playwright doing things, and as part of the ux review skill, taking & verifying the screenshots against the written specs. Likewise, as many others, we vibe coded the flows to set all that up and tweak it over time. When we hit prod issues or scraping tasks, we sometimes do similar. In some of our envs, we don't have playwright, so do it other ways.
Now imagine a million developer using claude code, how many of them are doing web & frontend stuff, and what the data flywheel looks like there. So how much is really needed for this use case to be native?
? Very much agreed, the IPO pop is a manufactured pricing event focused on investor dynamics rather than direct fair market pricing, making it more of a gamble than normal. Including gambles in index funds defeats the point.
Maybe the confusing point was my involvement is (discounted) pre-IPO shares, which almost by definition, is not an activity accessible to retail investors.
Mostly by having a pulse for the last 10-20 years as someone in the bay area seeing it repeatedly play out as tech IPOs get dumped onto retail investors repeatedly, including the 'good' ones. Being lucky enough to participate in IPOs makes you check these wrt when to balance IPO pop exit (weeks/months) vs long-term tax benefits of holding (2yr+).
- The initial pop is known to be manufactured by banks, so mostly benefits insiders, so good time to diversify. I'm conservative so sold to cover effective basis or whatever risk strategy :)
- The lockup period (6mo) is a similarly known artificial event, and studies show that
- Tech companies take ~8 quarters of prep for the IPO as they do financial engineering to transition from VC growth-at-all-costs to public $, and I'd expect the same for whatever nonsense they pulled to juice numbers to shake out. And that's not including oddballs like the Musk alternate universe, just normal tech companies covering up EBITDA and low interest rate madness.
- Tech is especially volatile as an industry, so even more skepticism here. Eg, the latest IPO I was involved in was a successful professional social network play, and chatgpt killed it.
4-8 quarters for most tech IPOs to settle. IPOs are manufactured for the good times around young co's, so not surprising, and economic stability isn't a question of days/weeks/months.
And yes often a falling knife
This is pretty predictably wall street & federal regulators scamming normal people, retirement funds, etc, taking their fees and exit window at everyone else's expense
R1 work generally doesn't have a replication crisis, and generally incrementalism is the bigger issue there, which is in turn tied to penny pinching
The bigger issue is failure to significantly increase r&d funding, vs last decade+ shrinkages and Trump-era eating of the young, and focuses like you now propose suggest a continuation of such economy-inhibiting thinking. Also, note how your post was goalpost moving. This in turn is classic trolling with asymmetric effort, so I don't see your response in good faith.
- With no workers working, no worker fraud problem, sure. If you cut core scientific processes, politicize science, and destablize paycheck predictability enough to chase everyone good out of science, then yes any small amount of waste is also caught in the cuts.
- This seems to increase what you call bad "fun": Increases abuse of tax funding being corruptly given to projects advocated by political appointees despite rejection by scientific peer review. Vicious feedback loop.
Fwiw, the cost per answer, which is what ultimately matters, is going down. In a competitive market with oss and multiple frontier labs, it is hard to maintain a premium long-term.
The big question is how subsidies vs technology improvement will play out. As we saw with Uber, selling at a loss can happen for a very long time, and technology improves relentlessly.
For reference, we publish https://botsbench.com/ that shows time and cost per answer are going down while quality is going up.
oss models don't directly matter when multiple at-scale frontier API providers have to compete on price: they are limited in defensible margin
They do matter in that oss researchers enable faster cross-pollination of good inferencing efficiency improvements to help the big boys adapt ideas from the community
Long-term local ai may matter more, but imo not there until models + hw get way better (1-2 years?) . Reasoning grade quality at speed is still $$$: we need fast opus, not slow sonnet.
Twitter: LMeyerov
We're 100X'ing investigations with the first GPU visual graph AI platform and now Louie.AI, the genAI-first rethink of analyst notebooks. We enjoy working with data teams all the way from from tech companies and startups to scientists and government agencies, and on problems from threat hunting, fraud, & misinformation to supply chain, user journey, and genomics. Our partners include Amazon, Nvidia, and more.
* Louie.ai: Louie connects to your data systems so analysts can use natural language to ask questions and get back data, analyses, AI models, interactive visualizations, and everything else the Graphistry platform can do
* Graph visual analytics: Gartner measured graph as a Top 5 growing data technology for the next 5 years and awarded us as the 2021 Cool Vendor in Graph
* End-to-end GPU computing: Apache Arrow & RAPIDS.ai were both voted top data projects of 2020 & 2021
* Point-and-click workflow automation: Process automation is the fastest growing enterprise industry in history
* Graph AI (GNNs) to make it easy for operational teams to take the next step of their graph journey by automating their graph insights for tasks like detection: Winner of the 2022 US Cyber Command AI alert data challenge!
... And we're hiring! LLM app/backend engineering, AI (cyber, GNNs, ...), sales engineering, platform engineering, UI, and more: https://www.graphistry.com/careers