I'm a freelance software engineer with a strong background in early-stage product development, cloud infrastructure, and machine learning. My primary focus is driving multiplicative productivity across teams by implementing scalable, well-tested solutions to ambiguously defined business problems.
What I can offer:
- Building and scaling cloud infrastructure
- Early-stage product development and leadership in building technical teams
- Optimizing engineering workflows and ensuring developer productivity
If you need help scaling a tech stack, developing a new product, or driving efficiency across your team, I’d love to chat!
This was a great read and conscious of the nuances to effective writing. Articles like these tend to have a "write this way NOT this other way" mentality. To paraphrase a comment I saw somewhere regarding code optimization:
"Given enough time most code can be optimized quite a lot".
The same can be said about writing. The audience, the topic, and the intended outcome make for a complex optimization problem that, as the author touches on, can eat up a ton of time. The PR comment example is perfect. The initial comment (on the lefthand side) works. *It does the job*, however when the author is placed in a different context (audience and intent), the comment on the right becomes more optimal. It also took 5x the amount of time to write up. I feel as though knowing the tradeoffs and managing your own time seems to be half the battle when it comes to communicating.
My additional advice for devs is; get honest feedback on your writing (slack messages, design documents and everything in between) so that you can learn best what works with whom.
Yep, pretty much exactly this. I've spent some time with SLAM, and in my experience, it's usually just a part of a larger ML pipeline. Not only can SLAM be paired with external data sources to generate labeled 3D objects, but inferences can be drawn directly from a generated point cloud without any ML involved.
My best guess for this is that floor plans and furniture can be used to _fingerprint_ a user, rather than knowing that the user owns a couch so that The Company can recommend a particular CPG. Knowing who their target demographics are with greater accuracy certainly feeds back into their recommender systems, but not so directly, which makes me concerned for this investigation's success.
@dang, can we link the actual journal article[0] instead of the embellished advert to eat more red meat?
Also, generally, diet is political in nature and I don't see its place here on HN. Had this article focused on something else, like the replication crisis as it pertains to nutrition research (like some of the comments seem to think), instead of promoting specific dietary views, I could see a healthy discussion starting. However, as it stands, there is nothing in the comments that is related to the article and productive. This seems to be a common trend, I can email you a list of links, if you're interested.
---
For my own edification; some thoughts after reading the journal, the comments and some supplementary links. I'm trying not to put on a tinfoil hat and I could be missing the point on some of these.
- The author performs funded research by and holds shares in an agri-tech company with a focus on poultry and swine[1]. This isn't an immediate conflict of interest in my mind (food professionals are commonly hired by agribusinesses), but just glancing at her previous publications[2], it's hard to find her arguing against livestock consumption in any way.
- What exactly are the 'serious errors' mentioned, specific to red meat consumption? The article's primary concerns seems to be that the GBD 2019 risk factor analysis[3] a) Changes the TMREL (theoretical minimum risk exposure level) for risks to be 0 (this isn't just for red meat) b) Categorizes red meat as a risk factor in the first place c) Fails to match up with other systematic reviews put up by other research organizations. One organization mentioned, NutriRECS[4], has substantial ties to the meat industry[5]
- The article fails to mention the fact that GBD groups "dietary factors" into one when linking to death, AND that there was decrease in diet-related burdens[6]. I don't see anywhere that the GDP study says red meat definitively equals more death. It's mentioned as an increased risk factor, but not elaborated on. Additionally, I don't know where this article's authors got their "36-fold" increase from. Could someone help me find that?
- I'm confused more about what a quality diet should be after reading this piece. So, it seems as though the article is successful.
These thoughts are not entirely my own and I encourage you, along with every one of the commenters here, to read "Food Politics" by Marion Nestle. The dietary advice is generally sound, but the overall message of the book holds true; diet is a political issue. It's a particularly polarizing one too, due to the intimate role that food plays in our cultures, identities, livelihoods and health. It makes sense that this article (along with a hoard of others), is so opinionated and lacks the context to have a well-rounded analysis. I didn't read all the studies, and I'm not going to tell you what to eat, but here my thoughts after reading the article and comments;
- The article mentions data collection issues in nutritional research. My understanding is that, generally, most of this data is collected at an aggregate level (economic I/O, based on overall production and consumption), or via the FFQs mentioned. Self reporting can be unreliable and is heavily subject to biases and the placebo effect.
- The studies linked about TMAO[0] seem to follow a couple trends in nutritional research that food companies rely on to sell more food. First is; diet is holistic, but applying pharmaceutical methodologies to individual nutrients while ignoring the overall dietary habits (again, data collection is hard) can lead to opportunities for companies to market unhealthy food as "healthy". I see that red heart[1] on some of the most sugary cereals, still. Second is; heightening the focus of specific nutrients over food sources in general as a method of dietary confusion. It is more confusing, and more favorable to industry, to say "avoid foods containing cholesterol and saturated fats," than it is to say "avoid meat". Studies that focus on nutrients reinforce these claims and make sound dietary advice inaccessible.
- The article fails to disclose the author's own biases[2] while calling out the "conflicts of interest" that the other studies' authors have with P&G. Commenters are quick to say "follow the money." However, a disclosed conflict-of-interest is not necessarily a bad thing, nutritional professionals need to work and the food industry is a, well... industry that hires food professionals. Do the conglomerates that fund nutrition-research often times leverage studies in bad-faith in order to sell more product? Yes, absolutely. Should you discredit all nutrition research backed by a corporation, probably not.
- Dietary advice is intended for populations, not individuals. If I were to say something like; "avoid having more than 24g of sugar a day"[3], I'm saying that targeting 97% of a population. This advice is also influenced by the goals of the organization/administration[4] releasing the advice and overall health concerns for the population. Most of the leading causes of death, in the United States, are linked with overconsumption. As an individual, genetics and lifestyle play a role and shift your needs. A person will probably need more than the recommended amount of salt on the day-to-day than average if they exercise.
On a separate note:
@dang I'm tired of reading political op-ed pieces that quote bad studies as proof of... whatever, especially those related to health. Is there a blocklist and can (unsettledscience.substack.com)[5] be added to that list? Also, out of curiosity and possibly related to this submission, what sort of mechanisms does HN employ in order to moderate submissions from suspect users? Thanks for all you do by the way.
I'm a freelance software engineer with a strong background in early-stage product development, cloud infrastructure, and machine learning. My primary focus is driving multiplicative productivity across teams by implementing scalable, well-tested solutions to ambiguously defined business problems.
What I can offer:
- Building and scaling cloud infrastructure
- Early-stage product development and leadership in building technical teams
- Optimizing engineering workflows and ensuring developer productivity
If you need help scaling a tech stack, developing a new product, or driving efficiency across your team, I’d love to chat!