It is a simple shorthand, command- and token-based domain-specific language that may help doctors and clinicians write structured clinical case notes and histories faster.
It will convert these short notes into SOAP notes (currently available), and later into Markdown, JSON, and PDF.
I do not intend to make it a clinical decision support tool, as that would impose many restrictions.
I am positioning it as a note-taking tool for doctors, medical students, and residents.
I understand that ambient AI tools are now available for faster case documentation, but they may not be usable everywhere, especially in busy and noisy hospital environments.
I am using AI agents to write most of the code, while I supervise the app architecture, editor UI, and the system for easily adding plugins.
Regarding plugins, I plan to implement them as additional commands and tokens specific to departments\u2014for example, obstetrics and gynecology-specific commands, and ICU-specific commands.
I have implemented the parsing engine in Go, and the front end is built using React + Vite for the web application.
It is still incomplete and highly bloated with AI slop (yes I accept that) but once I make the satisfying version. I am willing to do the trimming later on.
You can take a look at the code here : https://github.com/ppnpm/clinlang
I am open to suggestions, feedback and criticism.\nI know I might get backlash over AI use but I am still learning GO. (I am from JavaScript background)"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Clinglang \u2013 A shorthand language for doctors to write structured cases"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/ppnpm/clinlang"}},"_tags":["story","author_ppnpm","story_48333114","show_hn"],"author":"ppnpm","created_at":"2026-05-30T06:03:22Z","created_at_i":1780121002,"num_comments":0,"objectID":"48333114","points":1,"story_id":48333114,"story_text":"I am building a language, parser, and a simple editor to write this language.
It is a simple shorthand, command- and token-based domain-specific language that may help doctors and clinicians write structured clinical case notes and histories faster.
It will convert these short notes into SOAP notes (currently available), and later into Markdown, JSON, and PDF.
I do not intend to make it a clinical decision support tool, as that would impose many restrictions.
I am positioning it as a note-taking tool for doctors, medical students, and residents.
I understand that ambient AI tools are now available for faster case documentation, but they may not be usable everywhere, especially in busy and noisy hospital environments.
I am using AI agents to write most of the code, while I supervise the app architecture, editor UI, and the system for easily adding plugins.
Regarding plugins, I plan to implement them as additional commands and tokens specific to departments\u2014for example, obstetrics and gynecology-specific commands, and ICU-specific commands.
I have implemented the parsing engine in Go, and the front end is built using React + Vite for the web application.
It is still incomplete and highly bloated with AI slop (yes I accept that) but once I make the satisfying version. I am willing to do the trimming later on.
You can take a look at the code here : https://github.com/ppnpm/clinlang
I am open to suggestions, feedback and criticism.\nI know I might get backlash over AI use but I am still learning GO. (I am from JavaScript background)","title":"Show HN: Clinglang \u2013 A shorthand language for doctors to write structured cases","updated_at":"2026-05-30T06:06:54Z","url":"https://github.com/ppnpm/clinlang"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"westurner"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["clinical","decision","support","agent","ai","medical"],"value":"Yeah, so it's at that workflow where they're not getting linked data edges out of their own unstructured data input.
To prompt them at that time for whether or not this referenced thing in a dictated .txt file is actually a thing with a URI and have them confirm that those are the correct annotations for their input would save a lot of time and money.
Requisite study for Linked Data clinical coding / informatics:
- HIPAA and unstructured notes, HIPAA and Linked Data; Informed Consent; Precision Medicine
- Tokens; NLP, stemming, conceptual entity recognition
- The LODcloud; a great big graph of Linked Open Data (that our data does not yet link to, is siloed separately from, does not yet have references to existing URIs in)
- RDFa: RDF-in-html-Attributes
- JSON-LD: JSON Linked Data
- Schema.org/MedicalEntity ; /docs/schemas.html -> "Health and medical types" https://schema.org/docs/meddocs.html
- TTS Text-to-Speech, Speech-to-Text; Multimodal models with RAG, LORA, RLHF, Transformer Self Attention tensor Networks, Benchmarking OpenAI Whisper with relative performance metrics, ONNX
- FHIR for EHR data portability (JSON-LD,)
UX Improvements:
- More IDE-like unstructured data entry
- Auto-annotate this text field in place
- Auto-annotate this pasted text
- Auto-annotate this text field and display the linked data annotations
- Display the revisions of the unstructured note -> annotated linked data document
- Autocomplete with search from e.g. icd:
- Frequently used annotations per Clinician/Provider
- Recently used annotations per Clinician/Provider
- Hover over visually distinguished auto-recognized entities and approve/clarify/reject
- Indicate which prov:Agent added which annotations; human (name(s)), AI (signed git revision of repo URI)
- Indicate degree of confidence in annotation (note that AGI hypergraph systems have TruthValue and also AttentionValue, like attention networks; and that AGI systems may someday also support CDSS: Clinical Decision Support Systems and Health Decision Support Systems)"},"story_title":{"matchLevel":"none","matchedWords":[],"value":"Snomed CT Entity Linking Challenge"},"story_url":{"matchLevel":"none","matchedWords":[],"value":"https://www.drivendata.org/competitions/258/competition-snomed-ct/"}},"_tags":["comment","author_westurner","story_38739890"],"author":"westurner","children":[38744686],"comment_text":"Yeah, so it's at that workflow where they're not getting linked data edges out of their own unstructured data input.
To prompt them at that time for whether or not this referenced thing in a dictated .txt file is actually a thing with a URI and have them confirm that those are the correct annotations for their input would save a lot of time and money.
Requisite study for Linked Data clinical coding / informatics:
- HIPAA and unstructured notes, HIPAA and Linked Data; Informed Consent; Precision Medicine
- Tokens; NLP, stemming, conceptual entity recognition
- The LODcloud; a great big graph of Linked Open Data (that our data does not yet link to, is siloed separately from, does not yet have references to existing URIs in)
- RDFa: RDF-in-html-Attributes
- JSON-LD: JSON Linked Data
- Schema.org/MedicalEntity ; /docs/schemas.html -> "Health and medical types" https://schema.org/docs/meddocs.html
- TTS Text-to-Speech, Speech-to-Text; Multimodal models with RAG, LORA, RLHF, Transformer Self Attention tensor Networks, Benchmarking OpenAI Whisper with relative performance metrics, ONNX
- FHIR for EHR data portability (JSON-LD,)
UX Improvements:
- More IDE-like unstructured data entry
- Auto-annotate this text field in place
- Auto-annotate this pasted text
- Auto-annotate this text field and display the linked data annotations
- Display the revisions of the unstructured note -> annotated linked data document
- Autocomplete with search from e.g. icd:
- Frequently used annotations per Clinician/Provider
- Recently used annotations per Clinician/Provider
- Hover over visually distinguished auto-recognized entities and approve/clarify/reject
- Indicate which prov:Agent added which annotations; human (name(s)), AI (signed git revision of repo URI)
- Indicate degree of confidence in annotation (note that AGI hypergraph systems have TruthValue and also AttentionValue, like attention networks; and that AGI systems may someday also support CDSS: Clinical Decision Support Systems and Health Decision Support Systems)","created_at":"2023-12-23T13:43:47Z","created_at_i":1703339027,"objectID":"38744177","parent_id":38740869,"story_id":38739890,"story_title":"Snomed CT Entity Linking Challenge","story_url":"https://www.drivendata.org/competitions/258/competition-snomed-ct/","updated_at":"2024-09-20T16:00:01Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"minimaxa"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["clinical","decision","support","agent","ai","medical"],"value":""Teaching machines to love"
Your talking about androids...
Replicant Nexus 6: a basic pleasure model intended for military personnel.
I see where this is going, Silicon Valley nerds. Lol
AI advising how human meat proxies can survive in an AGI-slop world:
1) Lock down your own stack (1\u20133 days)\nTask: Harden your personal and business infrastructure against agentic attacks.\nWhy now: Agents are becoming superhuman at breaking in/out of systems; the first victims are poorly secured devs/founders.
Do this:
Enforce passkeys + hardware 2FA everywhere; rotate secrets; use short\u2011lived credentials.
Isolate dev/stage/prod; least\u2011privilege API keys; audit MCP/tools your agents can call.
Add immutable logs and approval gates for any agent action that touches money, data exports, or production.
Profit link: You avoid catastrophic loss and can credibly sell \u201cagent\u2011safe\u201d setups to others.
2) Turn one expensive workflow into a measured ROI agent (1\u20132 weeks)\nTask: Pick a single, costly, repetitive process (yours or a client\u2019s) and instrument it end\u2011to\u2011end before automating.
Why now: Buyers pay for calculable ROI, not \u201cAI magic.\u201d Vertical, single\u2011workflow agents are the most bankable in 2026.
Do this:
Map steps, baseline hours/$ lost (e.g., slow lead reply, invoice chasing, support triage).
Build the smallest agent that moves the metric (Make/n8n + LLM is enough).
Run on real data 2\u20134 weeks; measure bookings/sales/hours saved; only then scale or productize.
Profit link: Immediate time\u2011to\u2011cash via retained hours or extra sales; becomes a repeatable offer.
3) Specialize in a vertical where you can speak the business language (2\u20136 weeks)\nTask: Choose one industry with expensive back\u2011office pain (law contracts, medical billing, insurance claims, freight exceptions, trades scheduling).
Why now: Horizontal \u201cAI for everyone\u201d is crowded; vertical agents with clear ROI win.
Do this:
Shadow 3\u20135 operators; document their workflow, compliance constraints, and failure modes.
Build a narrow agent that owns one sub\u2011process end\u2011to\u2011end with approvals.
Price on value (e.g., % of recovered revenue or fixed fee per processed claim).
Profit link: Higher pricing power, stickier contracts, and easier referrals inside a niche.
4) Add AI security as a core service (4\u20138 weeks)\nTask: Learn and offer prompt\u2011injection defense, LLM/agent red\u2011teaming, MCP/tool security, and AI supply\u2011chain checks.
Why now: 78% of cybersecurity jobs now require AI skills; firms need people who can direct, constrain, and verify agent work.
Do this:
Study OWASP Top 10 for LLMs, MITRE ATLAS; practice with PyRIT/Garak/Lakera.
Add tool\u2011invocation audits, skill provenance checks, and least\u2011privilege patterns to your agents.
Package a \u201csafe agent deployment\u201d audit + hardening retainer.
Profit link: You become the person who lets companies adopt agents without getting pwned\u2014high demand, low supply.
5) Build a verification layer: human\u2011in\u2011the\u2011loop control planes (6\u201310 weeks)\nTask: Design approval workflows, evidence checks, and uncertainty flags so agents can\u2019t act unilaterally on high\u2011stakes decisions.
Why now: As models generalize, the risk shifts from the model to the surrounding system; verification is the moat.
Do this:
Require human approval for consequential actions (money, data exfil, config changes).
Force agents to produce evidence bundles (logs, retrieved docs, reasoning summaries) before action.
Track false positives, missed evidence, and unsafe actions; publish reliability metrics.
Profit link: Enterprises will only scale agents that pass audit; you sell the control plane and the audit trail.
6) Productize your best workflow as a micro\u2011SaaS/agent subscription (2\u20134 months)\nTask: Turn a proven client workflow into a repeatable, multi\u2011tenant agent with usage\u2011based pricing.
Why now: Services scale your time; productized agents scale your code and ops.
Do this:
Standardize the workflow, integrations, and permissions; strip client\u2011specific logic.
Add tenant isolation, billing, and observability; keep narrow scope.
Sell as setup fee + monthly retainer or per\u2011task pricing.
Profit link: Recurring revenue with defensible niche positioning.
7) Become an \u201cagent integrator\u201d for critical systems (3\u20136 months)\nTask: Offer end\u2011to\u2011end agent deployments into cloud/identity/network stacks with secure patterns (short\u2011lived creds, network controls, logging).
Why now: AI workloads run in the cloud; cloud security is a top skills gap second only to AI itself.
Do this:
Master IAM, VPC/network segmentation, secrets management, and SIEM integration for agent actions.
Provide runbooks: what the agent can/can\u2019t do, escalation paths, and failure modes.
Bundle training for their team on supervising agents.
Profit link: Large contracts with stickiness; you\u2019re the bridge between AI and core infra.
8) Create an \u201cAI safety case\u201d practice for regulated industries (6\u201312 months)\nTask: Help firms build documented safety cases: risk maps, governance, monitoring, and incident response for agentic systems.
Why now: Frameworks like NIST AI RMF and ISO/IEC 42001 are becoming baseline; regulators and boards demand this.
Do this:
Map AI use cases to risks (prompt injection, data leakage, unsafe generalization).
Implement monitoring (CoT/activation checks where possible), audit logs, and third\u2011party review processes.
Produce a living safety dossier tied to business impact.
Profit link: High\u2011margin consulting + ongoing compliance retainers; you\u2019re the \u201cadult in the room.\u201d
9) Own a data/evaluation moat in your vertical (6\u201318 months)\nTask: Collect real\u2011world agent telemetry, failure cases, and outcome data in your niche; build eval suites that buyers trust.
Why now: As models generalize, empirical validation matters more than theory; evals become the gate to deployment.
Do this:
Instrument every agent run: inputs, tools called, permissions used, outcomes, human overrides.
Publish reliability dashboards and benchmark against alternatives.
License eval datasets or charge premium for \u201cproven in the wild\u201d agents.
Profit link: Data network effects; competitors can\u2019t match your evidence base.
10) Position for the RSI era: automated AI research + human governance (12\u201324 months)\nTask: Build or join a team that automates AI improvement but keeps humans in the loop for alignment, monitoring, and pacing decisions.
Why now: Recursive self\u2011improvement is the logical endpoint; the winners will be those who can steer it safely.\nDo this:
Invest in tooling that auto\u2011generates/evaluates model edits, alignment tests, and monitoring upgrades.
Formalize governance: approval gates, third\u2011party audits, and responsible scaling policies.
Maintain strategic human oversight on capability jumps and deployment boundaries.
Profit link: Equity\u2011level upside; you\u2019re part of the core loop that compounds intelligence safely."},"story_title":{"matchLevel":"none","matchedWords":[],"value":"An Alien Mind"},"story_url":{"matchLevel":"none","matchedWords":[],"value":"https://openai.com/index/an-alien-mind/"}},"_tags":["comment","author_minimaxa","story_49588080"],"author":"minimaxa","comment_text":""Teaching machines to love"
Your talking about androids...
Replicant Nexus 6: a basic pleasure model intended for military personnel.
I see where this is going, Silicon Valley nerds. Lol
AI advising how human meat proxies can survive in an AGI-slop world:
1) Lock down your own stack (1\u20133 days)\nTask: Harden your personal and business infrastructure against agentic attacks.\nWhy now: Agents are becoming superhuman at breaking in/out of systems; the first victims are poorly secured devs/founders.
Do this:
Enforce passkeys + hardware 2FA everywhere; rotate secrets; use short\u2011lived credentials.
Isolate dev/stage/prod; least\u2011privilege API keys; audit MCP/tools your agents can call.
Add immutable logs and approval gates for any agent action that touches money, data exports, or production.
Profit link: You avoid catastrophic loss and can credibly sell \u201cagent\u2011safe\u201d setups to others.
2) Turn one expensive workflow into a measured ROI agent (1\u20132 weeks)\nTask: Pick a single, costly, repetitive process (yours or a client\u2019s) and instrument it end\u2011to\u2011end before automating.
Why now: Buyers pay for calculable ROI, not \u201cAI magic.\u201d Vertical, single\u2011workflow agents are the most bankable in 2026.
Do this:
Map steps, baseline hours/$ lost (e.g., slow lead reply, invoice chasing, support triage).
Build the smallest agent that moves the metric (Make/n8n + LLM is enough).
Run on real data 2\u20134 weeks; measure bookings/sales/hours saved; only then scale or productize.
Profit link: Immediate time\u2011to\u2011cash via retained hours or extra sales; becomes a repeatable offer.
3) Specialize in a vertical where you can speak the business language (2\u20136 weeks)\nTask: Choose one industry with expensive back\u2011office pain (law contracts, medical billing, insurance claims, freight exceptions, trades scheduling).
Why now: Horizontal \u201cAI for everyone\u201d is crowded; vertical agents with clear ROI win.
Do this:
Shadow 3\u20135 operators; document their workflow, compliance constraints, and failure modes.
Build a narrow agent that owns one sub\u2011process end\u2011to\u2011end with approvals.
Price on value (e.g., % of recovered revenue or fixed fee per processed claim).
Profit link: Higher pricing power, stickier contracts, and easier referrals inside a niche.
4) Add AI security as a core service (4\u20138 weeks)\nTask: Learn and offer prompt\u2011injection defense, LLM/agent red\u2011teaming, MCP/tool security, and AI supply\u2011chain checks.
Why now: 78% of cybersecurity jobs now require AI skills; firms need people who can direct, constrain, and verify agent work.
Do this:
Study OWASP Top 10 for LLMs, MITRE ATLAS; practice with PyRIT/Garak/Lakera.
Add tool\u2011invocation audits, skill provenance checks, and least\u2011privilege patterns to your agents.
Package a \u201csafe agent deployment\u201d audit + hardening retainer.
Profit link: You become the person who lets companies adopt agents without getting pwned\u2014high demand, low supply.
5) Build a verification layer: human\u2011in\u2011the\u2011loop control planes (6\u201310 weeks)\nTask: Design approval workflows, evidence checks, and uncertainty flags so agents can\u2019t act unilaterally on high\u2011stakes decisions.
Why now: As models generalize, the risk shifts from the model to the surrounding system; verification is the moat.
Do this:
Require human approval for consequential actions (money, data exfil, config changes).
Force agents to produce evidence bundles (logs, retrieved docs, reasoning summaries) before action.
Track false positives, missed evidence, and unsafe actions; publish reliability metrics.
Profit link: Enterprises will only scale agents that pass audit; you sell the control plane and the audit trail.
6) Productize your best workflow as a micro\u2011SaaS/agent subscription (2\u20134 months)\nTask: Turn a proven client workflow into a repeatable, multi\u2011tenant agent with usage\u2011based pricing.
Why now: Services scale your time; productized agents scale your code and ops.
Do this:
Standardize the workflow, integrations, and permissions; strip client\u2011specific logic.
Add tenant isolation, billing, and observability; keep narrow scope.
Sell as setup fee + monthly retainer or per\u2011task pricing.
Profit link: Recurring revenue with defensible niche positioning.
7) Become an \u201cagent integrator\u201d for critical systems (3\u20136 months)\nTask: Offer end\u2011to\u2011end agent deployments into cloud/identity/network stacks with secure patterns (short\u2011lived creds, network controls, logging).
Why now: AI workloads run in the cloud; cloud security is a top skills gap second only to AI itself.
Do this:
Master IAM, VPC/network segmentation, secrets management, and SIEM integration for agent actions.
Provide runbooks: what the agent can/can\u2019t do, escalation paths, and failure modes.
Bundle training for their team on supervising agents.
Profit link: Large contracts with stickiness; you\u2019re the bridge between AI and core infra.
8) Create an \u201cAI safety case\u201d practice for regulated industries (6\u201312 months)\nTask: Help firms build documented safety cases: risk maps, governance, monitoring, and incident response for agentic systems.
Why now: Frameworks like NIST AI RMF and ISO/IEC 42001 are becoming baseline; regulators and boards demand this.
Do this:
Map AI use cases to risks (prompt injection, data leakage, unsafe generalization).
Implement monitoring (CoT/activation checks where possible), audit logs, and third\u2011party review processes.
Produce a living safety dossier tied to business impact.
Profit link: High\u2011margin consulting + ongoing compliance retainers; you\u2019re the \u201cadult in the room.\u201d
9) Own a data/evaluation moat in your vertical (6\u201318 months)\nTask: Collect real\u2011world agent telemetry, failure cases, and outcome data in your niche; build eval suites that buyers trust.
Why now: As models generalize, empirical validation matters more than theory; evals become the gate to deployment.
Do this:
Instrument every agent run: inputs, tools called, permissions used, outcomes, human overrides.
Publish reliability dashboards and benchmark against alternatives.
License eval datasets or charge premium for \u201cproven in the wild\u201d agents.
Profit link: Data network effects; competitors can\u2019t match your evidence base.
10) Position for the RSI era: automated AI research + human governance (12\u201324 months)\nTask: Build or join a team that automates AI improvement but keeps humans in the loop for alignment, monitoring, and pacing decisions.
Why now: Recursive self\u2011improvement is the logical endpoint; the winners will be those who can steer it safely.\nDo this:
Invest in tooling that auto\u2011generates/evaluates model edits, alignment tests, and monitoring upgrades.
Formalize governance: approval gates, third\u2011party audits, and responsible scaling policies.
Maintain strategic human oversight on capability jumps and deployment boundaries.
Profit link: Equity\u2011level upside; you\u2019re part of the core loop that compounds intelligence safely.","created_at":"2026-09-06T21:58:26Z","created_at_i":1788731906,"objectID":"49591371","parent_id":49588080,"story_id":49588080,"story_title":"An Alien Mind","story_url":"https://openai.com/index/an-alien-mind/","updated_at":"2026-09-06T22:50:12Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"johnfernow"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["clinical","decision","support","agent","ai","medical"],"value":"Prior to ~2021 or so, I felt that many if not most in the EA community not only wanted to donate their money in the manner that most effectively saved lives, but also donate as much of their money as they could while still avoiding relative poverty. So many were extremely frugal, living in small homes or apartments, donating as much money as they could afford to purchase mosquito nets, vaccines, and deworming medication for others, as well as many donating one of their kidneys to strangers, all things I think most would find commendable.
Billionaires who live luxurious lifestyles and donate to prevent AI catastrophes was not something I was aware of at all prior to ~2021. I'm not sure how that ever became part of the movement.
In fact, longtermism in general wasn't something I was aware of until sometime around then. Purchasing mosquito nets, vaccines, and deworming medication has a demonstrable impact for saving lives and objectively reduces human suffering. In contrast, while AI, nuclear warfare, etc. can indeed kill billions, donating to those charities has absolutely no demonstrable impact. It's possible it has an impact, but it is near impossible to prove.
GiveWell states the following:
> GiveWell evaluates potential top charities along four main criteria: (1) Effectiveness, as supported by evidence the program the charity is implementing saves or improves lives; (2) cost-effectiveness, or how much \u2018bang for the buck\u2019 the charity offers in terms of lives saved or improved per donation; (3) room for more funding, or the charity\u2019s ability to put additional donations to use; and (4) transparency.
So certain charities likely do save lives, but would never be recommended by GiveWell because they aren't transparent enough to verify that. There has to be concrete proof in order to get a recommendation, which is a perfectly reasonable decision on their part, and something I thought was the felt among all in the EA community until I heard some advocating for charities to reduce the chances of AI catastrophes and nuclear warfare, but those people always felt like a fringe part of the community until they started getting more news coverage.
I'm not condemning longtermism outright, I think those who with the appropriate skills should work towards reducing the likelihood of nuclear warfare and AI induced disasters, but I don't see how it could possibly be considered "effective" altruism to donate money to charities tackling those issues considering we have not the slightest clue as to whether it's effective.
In a world where people die of preventable and curable diseases because governments choose not to spend more (or more effectively) on foreign aid, I absolutely believe it is critical that people take responsibility for saving those that they can. If I see someone drowning at the beach, I'm not going to say "Well I'm not going to save them because the government didn't pay for lifeguards." Sure, they probably should have, but at the end of the day someone is living or dying based on my decision: I'm going to try to save them. The same is true for those dying of malaria, neglected tropical diseases and malnutrition: yes, I believe governments should take responsibility for those issues, but they haven't, and if I don't donate what I can, more people will die. Hundreds of thousands die each year from malaria and NTDs and millions from malnutrition. While I'm not remotely capable of solving those issues, I absolutely am capable of saving some of those people, which certainly has an enormous impact on those I can save (also, while hundreds of thousands die from malaria each year, millions suffer from it but survive, so not only can reduce death but also pain.) I think trying to get more people to donate to charities that are demonstrably effective at saving lives is extremely important. With the "Effective Altruism" movement now including stuff like longtermism, as well as having its reputation ruined, I'm not sure how advocacy should move forward. On one hand, I don't think it'd ever be good for it to be an ideology: one should evaluate individual concepts by themselves, not tie them into some larger ideology and then decide based on one's personal feelings towards that ideology whether to support it. Still, it's helpful to have terms to describe concepts in fewer words, as well as have tangible goals for advocacy, as the more people that donate to charities that effectively save lives, the more lives that are saved overall."},"story_title":{"matchLevel":"none","matchedWords":[],"value":"Effective Altruism Is a Welter of Lies, Hypocrisy, and Eugenic Fantasies"},"story_url":{"matchLevel":"none","matchedWords":[],"value":"https://www.truthdig.com/articles/effective-altruism-is-a-welter-of-fraud-lies-exploitation-and-eugenic-fantasies/"}},"_tags":["comment","author_johnfernow","story_38244512"],"author":"johnfernow","comment_text":"Prior to ~2021 or so, I felt that many if not most in the EA community not only wanted to donate their money in the manner that most effectively saved lives, but also donate as much of their money as they could while still avoiding relative poverty. So many were extremely frugal, living in small homes or apartments, donating as much money as they could afford to purchase mosquito nets, vaccines, and deworming medication for others, as well as many donating one of their kidneys to strangers, all things I think most would find commendable.
Billionaires who live luxurious lifestyles and donate to prevent AI catastrophes was not something I was aware of at all prior to ~2021. I'm not sure how that ever became part of the movement.
In fact, longtermism in general wasn't something I was aware of until sometime around then. Purchasing mosquito nets, vaccines, and deworming medication has a demonstrable impact for saving lives and objectively reduces human suffering. In contrast, while AI, nuclear warfare, etc. can indeed kill billions, donating to those charities has absolutely no demonstrable impact. It's possible it has an impact, but it is near impossible to prove.
GiveWell states the following:
> GiveWell evaluates potential top charities along four main criteria: (1) Effectiveness, as supported by evidence the program the charity is implementing saves or improves lives; (2) cost-effectiveness, or how much \u2018bang for the buck\u2019 the charity offers in terms of lives saved or improved per donation; (3) room for more funding, or the charity\u2019s ability to put additional donations to use; and (4) transparency.
So certain charities likely do save lives, but would never be recommended by GiveWell because they aren't transparent enough to verify that. There has to be concrete proof in order to get a recommendation, which is a perfectly reasonable decision on their part, and something I thought was the felt among all in the EA community until I heard some advocating for charities to reduce the chances of AI catastrophes and nuclear warfare, but those people always felt like a fringe part of the community until they started getting more news coverage.
I'm not condemning longtermism outright, I think those who with the appropriate skills should work towards reducing the likelihood of nuclear warfare and AI induced disasters, but I don't see how it could possibly be considered "effective" altruism to donate money to charities tackling those issues considering we have not the slightest clue as to whether it's effective.
In a world where people die of preventable and curable diseases because governments choose not to spend more (or more effectively) on foreign aid, I absolutely believe it is critical that people take responsibility for saving those that they can. If I see someone drowning at the beach, I'm not going to say "Well I'm not going to save them because the government didn't pay for lifeguards." Sure, they probably should have, but at the end of the day someone is living or dying based on my decision: I'm going to try to save them. The same is true for those dying of malaria, neglected tropical diseases and malnutrition: yes, I believe governments should take responsibility for those issues, but they haven't, and if I don't donate what I can, more people will die. Hundreds of thousands die each year from malaria and NTDs and millions from malnutrition. While I'm not remotely capable of solving those issues, I absolutely am capable of saving some of those people, which certainly has an enormous impact on those I can save (also, while hundreds of thousands die from malaria each year, millions suffer from it but survive, so not only can reduce death but also pain.) I think trying to get more people to donate to charities that are demonstrably effective at saving lives is extremely important. With the "Effective Altruism" movement now including stuff like longtermism, as well as having its reputation ruined, I'm not sure how advocacy should move forward. On one hand, I don't think it'd ever be good for it to be an ideology: one should evaluate individual concepts by themselves, not tie them into some larger ideology and then decide based on one's personal feelings towards that ideology whether to support it. Still, it's helpful to have terms to describe concepts in fewer words, as well as have tangible goals for advocacy, as the more people that donate to charities that effectively save lives, the more lives that are saved overall.","created_at":"2023-11-12T23:36:45Z","created_at_i":1699832205,"objectID":"38245269","parent_id":38244699,"story_id":38244512,"story_title":"Effective Altruism Is a Welter of Lies, Hypocrisy, and Eugenic Fantasies","story_url":"https://www.truthdig.com/articles/effective-altruism-is-a-welter-of-fraud-lies-exploitation-and-eugenic-fantasies/","updated_at":"2025-08-14T22:53:02Z"}],"hitsPerPage":20,"nbHits":4,"nbPages":1,"page":0,"params":"query=clinical+decision+support+agent+AI+medical&advancedSyntax=true&analyticsTags=backend","processingTimeMS":36,"processingTimingsMS":{"_request":{"queue":5,"roundTrip":19},"afterFetch":{"format":{"highlighting":1,"total":1},"merge":{"mergeLoop":{"prepareNextHit":1,"total":1},"total":1},"total":1},"fetch":{"query":22,"scanning":11,"total":34},"total":36},"query":"clinical decision support agent AI medical","serverTimeMS":43}