stult

Every other country is responsible. But in proportion to their wealth and power, and the US is far and away the wealthiest and most powerful country in the world so we bear an outsized share of the responsibility for keeping the world stable and safe. We built the international order around free trade to suit us, and it works exceptionally well to enrich us, so we have a strong motivation to ensure the stability of that international order by reducing the causes of international conflict and civil disorder like hunger or disease.

Regardless, even if your amoral nihilism were correct rather than the hallmark of a morally repulsive psychopath with the imaginative capacity of a tapeworm, there were two things DOGE did wrong. First, much of the actual damage they caused was not from the US cutting aid per se, but rather how quickly and with such little warning they cut aid. DOGE denied aid recipients that were relying on the US to keep people alive with life saving medicine and food a reasonable opportunity to make alternative arrangements. People are dying not because the rest of the world is incapable of supplying ARVs to HIV patients in Africa, but because we took those critical life saving drugs away in a manner that made it impossible for the people depending on us to adapt. We killed many those people. You can't just stop taking ARVs and be OK, and someone make a few hundred dollars per year in rural Africa is not well positioned to find alternative suppliers. Many thousands of HIV positive pregnant women who would otherwise have been able to give birth to a child without the child contracting HIV now have to figure out how to survive HIV themselves and how to care for a child needlessly infected with HIV. Many of these people are now dead because of our negligence, arrogance, and stupidity. Because of your negligence, arrogance, and stupidity.

And it didn't even save us any money to do it that way, it was nothing less than abject cruelty and racism. DOGE let perfectly good drugs and food we had already paid for go to waste in warehouses rather than allow it to be delivered. For literally no reason, it saved us not a single penny and instead deprived many innocent people.

Above all, cutting aid like this was unbelievably stupid and self-defeating. Because even if psychopaths like you are objectively correct about reality (you aren't), when the world's richest man and the world's richest country murder millions of the world's poorest people for literally no reason, that makes us look really bad to the rest of the world. And then they do not cooperate with us. See, e.g., Trump begging the Europeans he so frequently attempts to bully for help with Iran. Idiots like you and Musk have trashed America's hard-earned reputation as benevolent superpower. That will cost us trade deals. It will force our allies to hedge against us by making trade deals with China instead as a counterbalance, as Canada has begun to do. The US is so phenomenally wealthy we can afford to be sociopathic assholes to the rest of the world for a little while at least, but it is difficult to overstate just how naive, ignorant, and outright moronic you are if you think that doesn't come at a price to American interests that far outweighs the negligible amounts of foreign aid spending DOGE illegally cut.

And I won't even get into the illegality of an unelected jackass impounding congressionally authorized spending because you do not seem like the sort of person who has any concept whatsoever of the value or importance of the rule of law and respect for the constitutional order.

A similar dilemma faces PACER. Overwhelmingly, PACER is used by attorneys, who are generally well-compensated professionals with a whole host of protectionist policies insulating them from market forces.

One of those protectionist policies is charging for PACER itself.

The costs of running PACER are absolutely trivial in comparison to the costs of running the judiciary. To the point that even bringing the point up is disingenuous to the point that it discredits everything else you say.

Case law is law. People are required to obey the law. They should be able to access the law so they can know how to follow it. It's that simple.

This analysis is almost bafflingly stupid.

They conflate domain expertise with coding expertise, and then assess that people with domain expertise demonstrate great success at coding tasks, which suggests coding agents are so good at writing code that domain experts can now cut software engineering experts out of the loop entirely. Yet if you look at their classifier, it classifies user expertise almost exclusively according to standards that measure expertise in coding. No wonder it predicts success at coding tasks. This just in: people who know how to develop software are better at developing software. What a fucking joke.

Literally does not work for me at all, just keeps reloading and spinning. I'm not even on my VPN. Really frustrating dealing with these things lately.

Man, I really freaking hate cloudflare bot checks. I can't even access this site, which I presume is just a few kilobytes of simple static HTML with some straightforward text content. I shouldn't have to work this hard to prove I'm human, it's exhausting

That's not true. ITAR and security clearance are entirely separate regulatory regimes. For ITAR purposes, being a permanent resident is good enough. I used to work for a defense contractor, and we hired plenty of green card holders. They were not in general assigned to work that required a clearance, but plenty of defense-sensitive, ITAR-controlled work is done by green card holders.

Troops is almost exclusively used to refer to Army personnel in the US.

I think warfighter crept into the lexicon for somewhat understandable reasons, likely because of the increasing frequency of joint operations (i.e., operations involving more than one branch of the military working together) after Vietnam, combined with the long-standing military tradition whereby members of any given branch take great offense if you refer to them using the wrong professional label (i.e., soldier, sailor, ~crayon-eater~ marine, airman, space cadet). That is, we can't just call all of them soldiers because only members of the Army are soldiers, so if for example you call a mixed group of marines and soldiers "soldiers", the marines will make their displeasure known to you, aggressively and in no uncertain terms.

When you're talking about DoD stuff all day long and frequently need to refer generically to the mixed personnel involved in a joint operation, warfighters beats saying Soldier-Sailor-Marine-Airman-Spacecase. All the other alternative phrases for the concept of "person employed by the military in one of the five combat arms branches" are variations on "member" and tend to sound clunky or be overly verbose, like "service member" or "member of the military." Try saying "service members" 50 times per day. Trust me, it gets old fast.

And frankly I don't see the problem with warfighter. Fighting wars is quite literally what they do, and pretending otherwise does a disservice to the truth and risks papering over the deadly seriousness of their work. Warfighter is also quite distinct from "warrior," which carries connotations of a specifically aggressive and barbaric flavor of professional violence purveyor. Like you say, it sounds like some atavistic hereditary soldier caste for whom violence is a sacred vocation joyfully undertaken rather than a solemn duty carried out only with great reluctance and forbearance.

- Do we have reasons to care about LOC in a world where we don't write code manually? What happens to token usage numbers when the codebase is significantly larger?

Yes, at least to the extent that we care about context windows and tokens consumed by coding agents processing code that is ultimately irrelevant to their assigned task.

Anecdotally, I've found keeping file sizes small has been important for agentic coding not just to maintain human readability, but also for optimizing agent performance, precisely because it limits the amount of incidental context they load while working a problem, because they generally load entire files rather than just parsing the part relevant to their current assignment as a human might. That smaller file size thus reduces input noise and the LLM generates a tighter solution, which in turn reduces input noise for future solutions. Or at least this strategy avoids a death spiral into exploding context length.

I expect (but cannot currently prove) that keeping overall LOC down yields similar benefits even when file sizes are kept small because it spares the LLM from parsing potentially relevant files that prove irrelevant to its current task.

I'm not sure about that, I actually think planning may be just as important in both domains. Outlining before drafting is an almost universal best practice in legal writing that is drilled into law students to the point that outlining as exam prep is something students spend several weeks on each semester. So personally I always have a fairly detailed implementation plan in the form of an outline before I ask an LLM to draft a more detailed legal document.

I've also adopted an AI coding workflow that involves a lot of planning, although I actually write very little of the plan myself anymore. I have a chain of slash commands like this: create-issue -> plan-issue -> build-plan -> pr-into-dev. I write a relatively brief description of what I want accomplished to create the issue, and then the agent fleshes out my description with more detailed requirements and acceptance criteria. I review the issue description, and the LLM often identifies open questions I failed to consider, so I revise as necessary and then the agent posts the description to the GH issue. I have planning separated because I often create issues quickly when something occurs to me and then circle back at a later date to implement, and want the agent to create the concrete implementation plan with an up-to-date snapshot of the code in context. Then I review that again, adjusting as necessary, and then the agent posts the result as a comment on the original issue.

Like you, I've found this detailed planning makes for a very robust coding agent (again, also in combination with the aforementioned best practices, especially requiring 100% test coverage because forcing it to exercise every line of code avoids hallucinated dummy tests that assert on nothing). Interestingly in comparison to legal writing, I also rely on the agent to decompose complex tasks into separate issues or subissues as appropriate, which is something that is never necessary for legal analysis because pretty much every every legal analysis can be one-shotted.

For legal writing, my workflow is nowhere near as structured as that. For context, I have only ever used LLMs for drafting what are effectively emails to clients or memoranda of law for clients that are a step up in complexity and formality from an email. So not something that will be filed with a court necessarily but very much in the same format and style as a formal motion that would be submitted to a court on behalf of a client. And never a contract, will, or judicial opinion, nor a communication with a counterparty like a demand letter or C&D. So YMMV for other types of legal writing.

That said, I typically start drafting a memo by conversing casually with an agent to explore the general boundaries of an issue I am evaluating, by identifying relevant sources of law, potentially related issues, and the analytical process I need to follow (i.e., what issues to evaluate and what order to evaluate them in, more or the less the analytical "algorithm"). Once I have a good sense of that algorithm, I put together a high level outline and then ask the agent to draft a detailed memo around that outline. Or at least that's what I used to do before the last few months, since when the models have matured to the point where I increasingly just ask the agent to write the outline based on the conversation we had, then review that, then ask it to write the memo based on the outline.

As I have been writing this, it occurs to me that actually I am following almost the exact same process for writing code and for writing legal memos, and should probably distill the legal writing process into a similarly well-structured set of chained skills/slash commands. In both domains, I describe an issue at a high level, get the LLM to fill in some of the broad outline level details, review that, then get the LLM to implement the complete final product. (Also perhaps worth noting while I do occasionally conduct general high level research by talking to a frontier lab LLM, I have always used locally hosted OS/OW models for drafting memos where I need to provide concrete, specific factual information about clients to the LLM, to avoid attorney-client privilege issues, so the quality has lagged behind the frontier models, which is part of why I haven't developed this workflow into as structured of an approach as I have for coding).

In both coding and legal contexts, I think that this planning or outlining step is critical not (or not just) because it forces the agent to create a higher quality product, but because it forces me to review what I am asking the agent to do at a sufficiently detailed level that I can catch errors before they crop up in the implementation. A lot of the time, the errors that occur if I skip this step aren't because the LLM has made any clear mistake, but because I failed to specify some aspect of the task and the LLM is forced to guess at what I really intended, which is where agents often struggle.

So I guess I would tentatively suggest that legal writing does in fact benefit from thorough planning, though it is hard for me to quantify whether those benefits are greater or less than the comparable benefits for code.

I could not agree more. A simple example: it boggles my mind how every state organizes their statutes in entirely dissimilar ways. I'm not sure there's a need for every state to have slightly different wording for a murder statute in the first place, but even assuming there is, why do they all have to be scattered around in different code sections instead of every state just following some consistent convention like always putting the murder statute at Title V, Section 1.4 (or whatever the case may be, that's just a random invented example).

For murder that's not such a huge deal because the statutes are typically easy to track down and don't really differ all that much substantively, but once you get really into the weeds on something like commercial contracts it can be a huge pain to do cross-jurisdictional research.

And that's just a tiny, super obvious example of how impenetrable statutory law is, which isn't even the really pernicious problem. Case law is infinitely worse. It makes me absolutely furious how difficult legal research still is. The Westlaw/LexisNexis duopoly is a moral crime and wildly destructive to the quality of government in this country. Every single written court opinion should be publicly available for free on the internet in an easily searched format. It would cost practically nothing to achieve. We're talking about less text than Wikipedia hosts. Yet still many states make it almost impossible to access case law. Even though these cases are law. Binding law that we are supposed to follow, yet we cannot even easily access. It's insane, and largely perpetuated by the complacency of lawyers who can charge others for what should be free, the lobbying of the duopoly, and the incompetence of politicians.

If all of the laws were consistently available and stored in reasonable, consistent citation formats (I would settle for hyperlinking as a replacement for the rat's nest of wildly varying jurisdiction-specific citation systems), it would even be possible to introduce a form of unit testing for legal drafting that would allow us to automatically verify if the LLM hallucinated a citation.

It also doesn't help that we (for what were at the time very good reasons) moved away from the system of legal writs that used to provide fairly standardized, almost "cut and paste" templates for legal filings. So now every legal document (filings, memos, contracts, court opinions, statutes) is drafted like a bespoke, artisanal creation with few strict structural or stylistic conventions. That makes automated interpretation much harder than it needs to be.

I would agree with this point and as I explained in a comment replying to the GP comment above, that atrophy is far more dangerous in the legal field than it is with code because legal documents do not benefit from the structural safeguards available for code, like automated testing, static typing, static analysis tools, etc. IME with legal LLMs so far, they are easily in that most dangerous valley where they can lull you into a false sense of security while still introducing extremely dangerous mistakes that are frequently difficult to detect without very careful reading.

The danger of those mistakes creeping in also grows exponentially the farther a lawyer strays from their core legal expertise. There are a few statutes I know inside and out, and I can spot LLM analytical errors related to them in a split second, but once I venture out into domains where I am not an expert (but where I am nevertheless reasonably qualified to practice), it becomes much harder to spot drafting mistakes because I have not refreshed my own understanding of the law by reviewing the relevant cases or statutes as I would when drafting the analysis myself from scratch.

IME so far (as both a lawyer and a software engineer), LLM error rates when drafting code and legal documents are reasonably comparable, but it's more problematic in the legal context because legal documents do not benefit from many of the structural safeguards available for code. For legal documents, there are no automated tests, no static typing, no test environments, no logging/observability instrumentation, no sandboxing.

The time lag between drafting and "deployment" also makes for much less effective, much more expensive debugging loops. You can deploy your code to prod in seconds, see an error pop up in the logs, and immediately start debugging. But it will take at a minimum days and frequently as long as several years before an error in a contract or a court filing will be detected, and often the error is beyond correction at that point. Thus, the errors are both more difficult to detect and to resolve.

And the consequences of error are often much greater, both because they are not correctable and because a legal error may risk someone's life, liberty, or substantial property. Although that's not categorically the case, obviously bugs in certain safety critical systems can be as bad or even worse than legal mistakes. But in general, most software is lower stakes than most legal writing.

On the flip side, LLMs do seem to do a better job with basic style and structure for legal documents compared to code. Things like following IRAC format, citing assertions of law (although hallucination remains an issue), and writing comprehensible sentences. These would be the equivalents in code to best practices like good comments, cohesion, consistent use of design patterns, test coverage, clear variable names, DRY, etc. Although the better performance on those more qualitative metrics may just be because even the longest legal documents are typically simpler in structure and have fewer lines of text than a large, complex codebase. Or maybe it's because LLMs are trained on natural language text more than on code. Or because natural language is more forgiving than code, in that minor variation in diction or grammar is unlikely to have any significant effect on how the document is interpreted, whereas even single character errors in code can have enormous effects.

That's not true at all. Modern legal education has focused on plain English drafting and avoidance of arcane jargon precisely to make legal documents comprehensible to non-specialists. There are almost no situations where legal drafting requires use of jargon. Jargon is pretty much only necessary where the domain requires use of jargon. Contracts are meant to be followed by the parties, and if the parties can't understand the terms of the contract because of obscure drafting, they can't abide by the terms.

Also legal language is in no way a programming language. And I would know, I'm a lawyer and a software engineer. It would actually be a dramatic improvement if lawyers were more consistent in their use of terms of art, but in practice there are very few terms of art that aren't either in general use or easily understood with a brief definition, and none are defined with anything like the precision or consistency of a programming language.

The R&D tax credit change actually took effect in 2022, and one of the few good things Trump's BBB did was reverse it

I think that study is the wrong framing of the problem for identifying economic returns on AI. We don't need AI to complete tasks perfectly, just to be able to generate a good enough approximation that is easy to review and correct such that an employee has to spend less time correcting AI's errors than they would spend producing the entire output from scratch. So it won't be a drop in replacement for an employee for another 4-10 years, but in the interim, will shift an employee's role from generating a complete solution to primarily reviewing and correcting an LLM-generated solution to get it from that 80-95% level (or whatever the starting point might be prior to 2029) to 100%.

At this point, the vast majority of the work required to make GenAI capable of producing that sufficiently reviewable/correctable content isn't improving model quality, but creating the harnesses, infrastructure, and workflows around the models. Companies aren't seeing returns yet because too many early adopting companies have conceived of AI as a drop in replacement for employees, or at least as a reason to cut staff immediately, without first building out the supporting systems needed to compensate for the inadequacies of the models.

That's an asinine comparison that completely ignores the underlying economic substance of the transaction. You can't pay yourself fees from a mortgage.

Weirdly the CIA actually does require case officers to get signed receipts from their assets for payments. Whether they verify the signatures is another question...

You're assuming the price won't come down as the tech matures. That seems like a big assumption, considering how quickly open weights models are catching up to frontier models, and how little effort has been invested so far in optimizing inference costs.

It's especially a crazy assumption to make relative to the costs of employing a human. The costs of paying an entry level employee are unlikely to go down at all, and even if those costs do decline, there's a floor they can't drop below (minimum wage at the extreme end), whereas companies are free to optimize agentic costs as close to zero as possible.

So you are assuming that a cost which is extremely susceptible to optimization but which no one has yet seriously attempted to minimize will remain perpetually above a cost which is much less susceptible to optimization, is already subject to enormous efforts to minimize, and has a legally mandated floor. That seems like a bad bet.

It's a declaration that corporate America intends to impose all the costs of AI on individuals. Cloudflare could have retrained people, found new roles for them, etc. Instead it is throwing them to the wolves.

It used to be unacceptable for companies to lay people off purely to improve their margins. It's a sign of how dark the times have become that now CEOs don't even question the acceptability of layoffs that aren't driven by financial distress. The idea that they owe any loyalty to their employees has disappeared entirely. And boy you couldn't have invented a more fitting name for an entitled prick of a CEO reveling in this neo-aristocracy: Prince.

I hope everyone who still works at Cloudflare takes note and starts looking for an exit, because it's clear their boss is just waiting for the first possible chance to cut them and they cannot rely on any glint of humanity from him. Longer term, I think these types of layoffs will prove counterproductive for exactly that reason. It is poisonous to company culture, and all but sure to drive away talent.

Also I have had to deal with multiple breaking bugs from Cloudlfare and their absolutely atrocious customer service over the past few months, so I pulled all my personal and professional sites. They are shitting out vibe coded crap and not providing even minimal customer service anymore, even for an account spending six figures with them annually. Which might not be a huge number by CF standards, but surely should be enough to offer at least some human customer service to handle breaking bugs. Instead, all I got was a chatbot that regurgitated their FAQ and then gave me an email address that was no longer in use and a phone number that was disconnected. It was bafflingly unprofessional. So clearly whatever changes they have made are not working, and Prince is just too thick to recognize he is set to tank the company.

Bad facts make for bad law

Yes, exactly, there's a reason the term is "continuum of care." There is no one-size-fits-all approach to solving addiction because, to quote Ted Lasso, all people are different people. Maybe some people do need to be involuntarily incarcerated, but many, many others would be able to recover with far less intrusive interventions.

Also we are chasing a lagging indicator by focusing exclusively on the homeless population. The vast majority of people who end up homeless because of addiction would have benefited from some far earlier, far milder form of intervention, or from the absence of something that actively drove them into addiction, e.g. some quack pushing oxycontin on them because Purdue Pharma promoted it as non-addictive. Or job loss because of offshoring pushing them into economic despair that then drives addiction, which they are unable to recover from because of the lack of affordable or accessible retraining or educational opportunities.

In many cases over the last 20-30 years, it was the combination of both job loss and careless opioid prescription that pushed people into an unrecoverable spiral, especially in the rust belt, where the opioid crisis hit the hardest. We may not have fixed the job loss side of the problem, but at least doctors aren't pushing pills the same way they were 10-20 years ago after Purdue's corporate downfall, so the number of people driven into addiction-mediated homelessness by that disaster should at least start tapering off soon. But if we don't help people before their lives fall apart with a continuum of support options that are accessible before they are in deep crisis, and are accessible to people who are beginning to spiral but don't yet appear to be in deep crisis, it will cost far more and be far more challenging to help them recover once they are on the street.

Agreed, and further, I'd argue the OP's division of LLM instructions into either process or outcome specification is a false dichotomy. My agentic process specification is about automatically specifying the outcomes that I would otherwise repeatedly have to tell the LLM to consider, like making sure test coverage is maintained, or that decisions are documented on the original Github issue. Or it's about correcting common failure modes, like when the agent spends an enormous amount of time running repo-wide tests while debugging a focused change, because the agent doesn't consistently optimize around the time-to-implement as an outcome. Arguably part of addressing those failure modes boils down to pure process in the sense that I specify a logical order for achieving the outcomes, e.g. creating a plan before implementing. But that is mostly to organize approval gates for my convenience, rather than structuring the agent's work per se.

I don't think it's going to come up with carfentanyl synthesis from first principles, but obviously they haven't cleaned or prepared the data sets coming in.

I mean, why not? If it has learned fundamental chemistry principles and has ingested all the NIH studies on pain management, connecting the dots to fentanyl isn't out of the realm of possibility. Reading romance novels shows it how to produce sexualized writing. Ingesting history teaches the LLM how to make war. Learning anatomy teaches it how to kill.

Which I think also undercuts your first point that withholding "forbidden" materials is the only way to produce a safe LLM. Most questionable outputs can be derived from perfectly unobjectionable training material. So there is no way to produce a pure LLM that is safe, the problem necessarily requires bolting on a separate classifier to filter out objectionable content.

This is exactly why I don't understand the libertarian right in the US. Large corporations are just as capable of the unaccountable abuses that large governments are.

This seems like a really easy problem to solve. Just don't give the LLM access to any prod credentials[1]. If you can't repro a problem locally or in staging/dev environments, you need to update your deployment infra so it more closely matches prod. If you can't scope permissions tightly enough to distinguish between environments, update your permissions system to support that. I've never had anything even vaguely resembling the problems you are describing because I follow this approach.

[1] except perhaps read-only credentials to help diagnose problems, but even then I would only issue it an extremely short-lived token in case it leaks it somehow

What a facile point. By your logic, literally nothing should be banned. Murder, rape, etc.

That had previously been my experience with CF too. In this case, I was migrating my domain over from the registrar, and updated the nameservers to point to CF as per the standard practice, then waited for CF to detect the updated DNS records. Two days later (well after DNS should have propagated) CF was still displaying an error saying the update to the DNS record for the domain hadn't been detected.

There's not a lot of UI surface area that a user can touch that can even theoretically affect the NS detection process because that process happens in CF entirely "under the hood" as it were. You more or less just have to wait for CF to detect the DNS changes. That said, I tried everything I could think of to try to trigger their detector to reset, including deleting and recreating the site from scratch in CF. After another few days of combing through CF docs and forums, and after changing and reverting every setting I possibly could, I concluded there was no workaround available to me as a user and tried to reach CF as I described above.

Having done this many times before, I am quite certain that I set the nameservers correctly. I even had two other very experienced engineers review what I had done to make sure I wasn't falling victim to some mental blindspot that prevented me from recognizing what the problem was. I think every SWE has had the experience of spending an enormous amount of time debugging a problem only to realize they mistyped a magic string somewhere, but for whatever reason their brain just straight up refused to recognize the typo, but unfortunately that was not the case here. The other engineers saw what I saw and also were unable to fix the problem.

I was subsequently able to set DNS up on Vercel without any trouble at all. Bottom line, the issue was almost certainly a bug in Cloudflare's code. That indicates a code quality problem to me, which, in combination with the reckless incompetence that it takes to try to automate customer support with a chatbot that doesn't even have accurate information about their own processes and basic contact information, never mind a reasonable escape hatch to actual human-provided support in unusual cases (even for a paying customer), has led me simply not to trust them to deliver a reasonable quality product anymore.

They didn't even maintain any mechanism for reporting bugs to them, which is just insanity because it means there is no way to inform them even in extreme cases like a critical security bug. I get that they want to cut costs by reducing the employees needed to deal with customer service complaints, but it costs practically nothing to have a little feedback form somewhere, especially now that an LLM can handle most user feedback processing. Or failing that, a functioning support email address or phone number. But they can't even clear that incredibly low bar.

All of these issues could have been avoided with a very limited application of ordinary common sense and foresight. Whoever programmed their chatbot did not take the time to set up a decent RAG system with up-to-date information about their support processes and how to contact them, even though that is an obvious requirement for a tech support chatbot. They should also have recognized the business risks posed by exposing their customers to a system which lacks any escape hatches for outlier cases requiring actual human support, which risks alienating customers like me by forcing us to jump through Kafkaesque bureaucratic hoops just to get simple problems addressed, and--even worse--making it impossible to resolve such problems after jumping through all their hoops. The team implementing this chatbot didn't even think to include a contact form as a last resort method for reporting problems to them when the chatbot gets in over its head.

Most people hate this kind of LLM-provided customer support without any human escalation options, because the bots often end up uselessly looping through some debugging steps that simply do not work for the customer's specific issue for whatever reason, which feels like slamming your head against a wall repeatedly. It's a truly infuriating user experience and is practically guaranteed to destroy the business's public goodwill and reputation.

All of which means they are gutting their customer service department following some process that lacks access to these very basic insights, which screams mismanagement to me.

I'm not exactly a huge customer, but between my personal and business sites, I plowed $45k into CF last year, and will spend not another penny on them this year, or ever again. Maybe that's not huge spend in the grand scheme of the tech industry, but at a minimum that amount of money should entitle me to some human-provided support. My annual spend alone could provide the budget for multiple offshored CSRs. If I am spending enough money to buy a car, the least they can do is let me send them an email when I have a problem instead of just throwing me to the wolves.

Ultimately, they have a much weaker moat now than at any point in the past, because LLMs make it so much easier to build out critical functionality in-house that previously would have been worth paying someone else to manage via a SaaS. And while I may not be a big enough customer for them to worry about in and of myself, I am also not the only person affected by these business practices. Every affected person increases the reputational harms suffered by Cloudflare, with another alienated customer like me bashing CF in posts like this or in conversations with their friends and colleagues in the industry. Those harms should be very concerning to CF's management because it is extremely difficult to recover lost goodwill.

A few weeks ago, I ran into a bug with Cloudflare's DNS server not detecting when I updated the records with the registrar. The bug was 100% on their end, entirely unsolvable by me, yet they have made it literally impossible to contact them to file a bug report. Their standard user help workflow dead-ended by forcing me to talk to their absolutely useless AI help chatbot, which proceeded to regurgitate their FAQ (inaccurately, uselessly), then referred me to a phone number that was disconnected/not in service, then gave me an email address that auto-replied it was no longer in use, then just looped back to the FAQ. There was no way for me to even send them an email to let them know they have a major bug.

I immediately pulled all my sites off of Cloudflare and I will never use that godawful nightmare of a company for anything ever again. If they can't even host a generic help bot without screwing it up that badly, why would I ever use them for anything at all, never mind an AI platform?

lol I very nearly included a rant about that but decided it was too far off topic. Not being able to smoke weed may be more of an obstacle these days though.