During the months since, we\u2019ve gathered a lot of feedback from early users and realized that what developers actually wanted was more like an easier-to-use Temporal with integrations.
Here\u2019s what we\u2019ve learned so far:
- Serverless timeouts make it hard for anyone to write reliable background jobs. So our current product makes that easy for Next.js and other full-stack React frameworks. Long-running server support is coming soon.
- We simplified the architecture to make it far easier to self-host. This was the most common comment in our previous Show HN.
- We made it much easier to contribute to. You can now add new API integrations for any service we don\u2019t already support. Either publicly (we appreciate PRs) or privately in your existing codebase.
We\u2019re open about what we\u2019re building (https://trigger.dev/changelog) and what we\u2019re planning on doing next (https://trigger.dev#roadmap) as we believe community feedback ensures that we\u2019re solving real problems.
So here\u2019s where we\u2019re at, and where we\u2019re headed:
[x] Easy self-hosting.\n \n [x] Serverless. Long-running Jobs on your serverless backend.\n \n [x] Integration kit. Build your own integrations, or use ours.\n \n [x] Bring-Your-Own-Auth. You can now authenticate integrations as your users.\n \n [x] Dashboard. View every Task in every Run.\n \n [x] Cloud service. No deployment required.\n \n [x] React hooks. Easily update your UI with Job progress.\n \n [x] React frameworks. Support for Next.js, Astro, Remix, Express.\n \n [ ] More frameworks. Support for SvelteKit, Nuxt.js, Fastify, Redwood.\n \n [ ] Background functions. Offload long or intense tasks to our infrastructure.\n \n [ ] Long-running servers. Use Trigger.dev from your long-running backend.\n \n [ ] Polling Triggers. Subscribe to changes without webhooks.\n \n [ ] And lots more\u2026\nhttps://github.com/triggerdotdev/trigger.dev"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Show HN: Trigger.dev V2 \u2013 a Temporal alternative for TypeScript devs"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://trigger.dev"}},"_tags":["story","author_eallam","story_37750763","show_hn"],"author":"eallam","children":[37751082,37751113,37752570,37752584,37752670,37752705,37752727,37753175,37753349,37753461,37754119,37755127,37755145,37755728,37757082,37758442,37758967,37759033,37759567],"created_at":"2023-10-03T12:07:19Z","created_at_i":1696334839,"num_comments":39,"objectID":"37750763","points":172,"story_id":37750763,"story_text":"Back in February, we posted a Show HN about building a \u201cdeveloper-first open source Zapier alternative\u201d (https://news.ycombinator.com/item?id=34610686). This was v1 of Trigger.dev.
During the months since, we\u2019ve gathered a lot of feedback from early users and realized that what developers actually wanted was more like an easier-to-use Temporal with integrations.
Here\u2019s what we\u2019ve learned so far:
- Serverless timeouts make it hard for anyone to write reliable background jobs. So our current product makes that easy for Next.js and other full-stack React frameworks. Long-running server support is coming soon.
- We simplified the architecture to make it far easier to self-host. This was the most common comment in our previous Show HN.
- We made it much easier to contribute to. You can now add new API integrations for any service we don\u2019t already support. Either publicly (we appreciate PRs) or privately in your existing codebase.
We\u2019re open about what we\u2019re building (https://trigger.dev/changelog) and what we\u2019re planning on doing next (https://trigger.dev#roadmap) as we believe community feedback ensures that we\u2019re solving real problems.
So here\u2019s where we\u2019re at, and where we\u2019re headed:
[x] Easy self-hosting.\n \n [x] Serverless. Long-running Jobs on your serverless backend.\n \n [x] Integration kit. Build your own integrations, or use ours.\n \n [x] Bring-Your-Own-Auth. You can now authenticate integrations as your users.\n \n [x] Dashboard. View every Task in every Run.\n \n [x] Cloud service. No deployment required.\n \n [x] React hooks. Easily update your UI with Job progress.\n \n [x] React frameworks. Support for Next.js, Astro, Remix, Express.\n \n [ ] More frameworks. Support for SvelteKit, Nuxt.js, Fastify, Redwood.\n \n [ ] Background functions. Offload long or intense tasks to our infrastructure.\n \n [ ] Long-running servers. Use Trigger.dev from your long-running backend.\n \n [ ] Polling Triggers. Subscribe to changes without webhooks.\n \n [ ] And lots more\u2026\nhttps://github.com/triggerdotdev/trigger.dev","title":"Show HN: Trigger.dev V2 \u2013 a Temporal alternative for TypeScript devs","updated_at":"2025-09-21T00:26:47Z","url":"https://trigger.dev"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"olefoo"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Screenshot-proof images via temporal 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dithering","updated_at":"2025-10-09T19:19:13Z","url":"http://blog.persistent.info/2012/12/screenshot-proof-images-via-temporal.html"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"memexy"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Crux: Open-source document database with bi-temporal graph queries"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://opencrux.com/"}},"_tags":["story","author_memexy","story_23493163"],"author":"memexy","children":[23493176,23504694,23505137,23505650,23507037,23507248,23508882],"created_at":"2020-06-11T20:50:44Z","created_at_i":1591908644,"num_comments":33,"objectID":"23493163","points":161,"story_id":23493163,"title":"Crux: Open-source document database with bi-temporal graph queries","updated_at":"2024-09-20T06:27:46Z","url":"https://opencrux.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ceeb"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Is It Time for the JavaScript Temporal API?"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"https://blog.openreplay.com/is-it-time-for-the-javascript-temporal-api/"}},"_tags":["story","author_ceeb","story_29712118"],"author":"ceeb","children":[29712166,29736806,29736942,29736994,29737194,29737274,29737368,29737433,29737469,29737762,29739138,29740061,29744516],"created_at":"2021-12-28T09:57:59Z","created_at_i":1640685479,"num_comments":100,"objectID":"29712118","points":156,"story_id":29712118,"title":"Is It Time for the JavaScript Temporal API?","updated_at":"2024-09-20T10:05:29Z","url":"https://blog.openreplay.com/is-it-time-for-the-javascript-temporal-api/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jka"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Temporal Tables PostgreSQL Extension"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"https://github.com/arkhipov/temporal_tables"}},"_tags":["story","author_jka","story_26748096"],"author":"jka","children":[26768072,26768217,26768220,26768281,26768527,26768539,26768610,26769218,26769235,26769438,26769720,26770457,26771804,26772438],"created_at":"2021-04-09T08:39:02Z","created_at_i":1617957542,"num_comments":27,"objectID":"26748096","points":153,"story_id":26748096,"title":"Temporal Tables PostgreSQL Extension","updated_at":"2026-07-13T13:42:14Z","url":"https://github.com/arkhipov/temporal_tables"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kodablah"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Temporal .NET \u2013 Deterministic Workflow Authoring in .NET"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"https://temporal.io/blog/introducing-temporal-dotnet"}},"_tags":["story","author_kodablah","story_35804220"],"author":"kodablah","children":[35804377,35804503,35804788,35804811,35804995,35807240,35807736,35807999,35809480,35811102,35811764,35811926,35812514,35820944],"created_at":"2023-05-03T16:18:51Z","created_at_i":1683130731,"num_comments":49,"objectID":"35804220","points":152,"story_id":35804220,"title":"Temporal .NET \u2013 Deterministic Workflow Authoring in .NET","updated_at":"2024-09-20T13:57:11Z","url":"https://temporal.io/blog/introducing-temporal-dotnet"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"aarestad"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Node.js 26.0.0 (Now with Temporal)"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://nodejs.org/en/blog/release/v26.0.0"}},"_tags":["story","author_aarestad","story_48212242"],"author":"aarestad","children":[48212567,48212944,48213013,48213297,48213717,48213780,48213869,48214000,48214036,48214517,48215186,48215548],"created_at":"2026-05-20T18:48:30Z","created_at_i":1779302910,"num_comments":61,"objectID":"48212242","points":151,"story_id":48212242,"title":"Node.js 26.0.0 (Now with Temporal)","updated_at":"2026-06-01T03:04:16Z","url":"https://nodejs.org/en/blog/release/v26.0.0"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"roseway4"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Hey HN! We're Paul, Preston, and Daniel from Zep. We've just open-sourced Graphiti, a Python library for building temporal Knowledge Graphs using LLMs.
Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context.
At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire chat history in a prompt. Accurate context is crucial for LLM applications. If an AI agent doesn't remember that you've changed jobs or confuses the chronology of events, its responses can be jarring or irrelevant, or worse, inaccurate.
Before Graphiti, our approach to storing and retrieving user \u201cmemory\u201d was, in effect, a specialized RAG pipeline. An LLM extracted \u201cfacts\u201d from a user\u2019s chat history. Semantic search, reranking, and other techniques then surfaced facts relevant to the current conversation back to a developer for inclusion in their prompt.
We attempted to reconcile how new information may change our understanding of existing facts:
Fact: \u201cKendra loves Adidas shoes\u201d
User message: \u201cI\u2019m so angry! My favorite Adidas shoes fell apart! Puma\u2019s are my new favorite shoes!\u201d
Facts:
- \u201cKendra used to love Adidas shoes but now prefers Puma.\u201d
- \u201cKendra\u2019s Adidas shoes fell apart.\u201d
Unfortunately, this approach became problematic. Reconciling facts from increasingly complex conversations challenged even frontier LLMs such as gpt-4o. We saw incomplete facts, poor recall, and hallucinations. Our RAG search also failed at times to capture the nuanced relationships between facts, leading to irrelevant or contradictory information being retrieved.
We tried fixing these issues with prompt optimization but saw diminishing returns on effort. We realized that a graph would help model a user\u2019s complex world, potentially addressing these challenges.
We were intrigued by Microsoft\u2019s GraphRAG, which expanded on RAG text chunking with a graph to better model a document corpus. However, it didn't solve our core problem: GraphRAG is designed for static documents and doesn't natively handle temporality.
So, we built Graphiti, which is designed from the ground up to handle constantly changing information, hybrid semantic and graph search, and scale:
- Temporal Awareness: Tracks changes in facts and relationships over time. Graph edges include temporal metadata to record relationship lifecycles.
- Episodic Processing: Ingests data as discrete episodes, maintaining data provenance and enabling incremental processing.
- Hybrid Search: Semantic and BM25 full-text search, with the ability to rerank results by distance from a central node.
- Scalable: Designed for large datasets, parallelizing LLM calls for batch processing while preserving event chronology.
- Varied Sources: Ingests both unstructured text and structured data.
Graphiti has significantly improved our ability to maintain accurate user context. It does a far better job of fact reconciliation over long, complex conversations. Node distance reranking, which places a user at the center of the graph, has also been a valuable tool. Quantitative data evaluation results may be a future ShowHN.
Work is ongoing, including:
1. Improving support for faster and cheaper small language models.
2. Exploring fine-tuning to improve accuracy and reduce latency.
3. Adding new querying capabilities, including search over neighborhood (sub-graph) summaries.
## Getting Started
Graphiti is open source and available on GitHub: https://github.com/getzep/graphiti.
We'd love to hear your thoughts. Please also consider contributing!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Show HN: Graphiti \u2013 LLM-Powered Temporal Knowledge Graphs"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/getzep/graphiti"}},"_tags":["story","author_roseway4","story_41445445","show_hn"],"author":"roseway4","children":[41445450,41448223,41449048,41449316,41454721,41455922,41456728],"created_at":"2024-09-04T13:21:19Z","created_at_i":1725456079,"num_comments":21,"objectID":"41445445","points":142,"story_id":41445445,"story_text":"Hey HN! We're Paul, Preston, and Daniel from Zep. We've just open-sourced Graphiti, a Python library for building temporal Knowledge Graphs using LLMs.
Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context.
At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire chat history in a prompt. Accurate context is crucial for LLM applications. If an AI agent doesn't remember that you've changed jobs or confuses the chronology of events, its responses can be jarring or irrelevant, or worse, inaccurate.
Before Graphiti, our approach to storing and retrieving user \u201cmemory\u201d was, in effect, a specialized RAG pipeline. An LLM extracted \u201cfacts\u201d from a user\u2019s chat history. Semantic search, reranking, and other techniques then surfaced facts relevant to the current conversation back to a developer for inclusion in their prompt.
We attempted to reconcile how new information may change our understanding of existing facts:
Fact: \u201cKendra loves Adidas shoes\u201d
User message: \u201cI\u2019m so angry! My favorite Adidas shoes fell apart! Puma\u2019s are my new favorite shoes!\u201d
Facts:
- \u201cKendra used to love Adidas shoes but now prefers Puma.\u201d
- \u201cKendra\u2019s Adidas shoes fell apart.\u201d
Unfortunately, this approach became problematic. Reconciling facts from increasingly complex conversations challenged even frontier LLMs such as gpt-4o. We saw incomplete facts, poor recall, and hallucinations. Our RAG search also failed at times to capture the nuanced relationships between facts, leading to irrelevant or contradictory information being retrieved.
We tried fixing these issues with prompt optimization but saw diminishing returns on effort. We realized that a graph would help model a user\u2019s complex world, potentially addressing these challenges.
We were intrigued by Microsoft\u2019s GraphRAG, which expanded on RAG text chunking with a graph to better model a document corpus. However, it didn't solve our core problem: GraphRAG is designed for static documents and doesn't natively handle temporality.
So, we built Graphiti, which is designed from the ground up to handle constantly changing information, hybrid semantic and graph search, and scale:
- Temporal Awareness: Tracks changes in facts and relationships over time. Graph edges include temporal metadata to record relationship lifecycles.
- Episodic Processing: Ingests data as discrete episodes, maintaining data provenance and enabling incremental processing.
- Hybrid Search: Semantic and BM25 full-text search, with the ability to rerank results by distance from a central node.
- Scalable: Designed for large datasets, parallelizing LLM calls for batch processing while preserving event chronology.
- Varied Sources: Ingests both unstructured text and structured data.
Graphiti has significantly improved our ability to maintain accurate user context. It does a far better job of fact reconciliation over long, complex conversations. Node distance reranking, which places a user at the center of the graph, has also been a valuable tool. Quantitative data evaluation results may be a future ShowHN.
Work is ongoing, including:
1. Improving support for faster and cheaper small language models.
2. Exploring fine-tuning to improve accuracy and reduce latency.
3. Adding new querying capabilities, including search over neighborhood (sub-graph) summaries.
## Getting Started
Graphiti is open source and available on GitHub: https://github.com/getzep/graphiti.
We'd love to hear your thoughts. Please also consider contributing!","title":"Show HN: Graphiti \u2013 LLM-Powered Temporal Knowledge Graphs","updated_at":"2026-08-31T21:32:39Z","url":"https://github.com/getzep/graphiti"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pron"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"TLA+ in Practice and Theory, Part 3: The Temporal Logic of Actions"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://pron.github.io/posts/tlaplus_part3"}},"_tags":["story","author_pron","story_14528072"],"author":"pron","children":[14531041,14531089,14531367,14531386,14531516],"created_at":"2017-06-10T17:00:19Z","created_at_i":1497114019,"num_comments":7,"objectID":"14528072","points":130,"story_id":14528072,"title":"TLA+ in Practice and Theory, Part 3: The Temporal Logic of Actions","updated_at":"2025-12-23T13:31:37Z","url":"https://pron.github.io/posts/tlaplus_part3"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"alecbenzer"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"MariaDB Temporal Data Tables"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"https://mariadb.com/kb/en/temporal-data-tables/"}},"_tags":["story","author_alecbenzer","story_23808444"],"author":"alecbenzer","children":[23808451,23809379,23809589,23809873,23810309,23810343,23811592,23811937,23812434,23812857,23813219],"created_at":"2020-07-12T03:04:14Z","created_at_i":1594523054,"num_comments":43,"objectID":"23808444","points":124,"story_id":23808444,"title":"MariaDB Temporal Data Tables","updated_at":"2024-09-20T06:32:12Z","url":"https://mariadb.com/kb/en/temporal-data-tables/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"50kIters"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Image Diffusion Models Exhibit Emergent Temporal Propagation in Videos"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://arxiv.org/abs/2511.19936"}},"_tags":["story","author_50kIters","story_46055177"],"author":"50kIters","children":[46056037,46057771,46061972,46064291],"created_at":"2025-11-26T07:55:49Z","created_at_i":1764143749,"num_comments":22,"objectID":"46055177","points":124,"story_id":46055177,"title":"Image Diffusion Models Exhibit Emergent Temporal Propagation in Videos","updated_at":"2026-03-05T23:08:00Z","url":"https://arxiv.org/abs/2511.19936"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"rgbrgb"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Temporal: open-source microservices orchestration platform"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"https://temporal.io/"}},"_tags":["story","author_rgbrgb","story_24815640"],"author":"rgbrgb","children":[24815905,24815998,24816391,24816859,24817682,24819317,24820185,24821819,24825572],"created_at":"2020-10-18T04:57:37Z","created_at_i":1602997057,"num_comments":52,"objectID":"24815640","points":123,"story_id":24815640,"title":"Temporal: open-source microservices orchestration platform","updated_at":"2026-02-06T17:09:21Z","url":"https://temporal.io/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"feross"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Retrofitting Temporal Memory Safety on C++"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"https://v8.dev/blog/retrofitting-temporal-memory-safety-on-c++"}},"_tags":["story","author_feross","story_31762574"],"author":"feross","children":[31786965,31787279,31787567,31788424,31788506,31788604,31790025],"created_at":"2022-06-16T05:02:50Z","created_at_i":1655355770,"num_comments":49,"objectID":"31762574","points":123,"story_id":31762574,"title":"Retrofitting Temporal Memory Safety on C++","updated_at":"2025-10-06T20:51:16Z","url":"https://v8.dev/blog/retrofitting-temporal-memory-safety-on-c++"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"vincentrolfs"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"The Temporal Dead Zone, or why the TypeScript codebase is full of var statements"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://vincentrolfs.dev/blog/ts-var"}},"_tags":["story","author_vincentrolfs","story_45437903"],"author":"vincentrolfs","children":[45467482,45467553,45467565,45468166,45468717,45468737,45468749,45469316,45469610,45471824],"created_at":"2025-10-01T14:06:45Z","created_at_i":1759327605,"num_comments":59,"objectID":"45437903","points":119,"story_id":45437903,"title":"The Temporal Dead Zone, or why the TypeScript codebase is full of var statements","updated_at":"2026-04-14T20:51:24Z","url":"https://vincentrolfs.dev/blog/ts-var"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"doener"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Reassessing relative temporal lobe size in anthropoids and modern humans"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://onlinelibrary.wiley.com/doi/10.1002/ajpa.24712"}},"_tags":["story","author_doener","story_35228219"],"author":"doener","children":[35228526,35228535,35228665,35228679,35228700,35228733,35228755,35228802,35228819,35228849,35228874,35228885,35228923,35229106,35229127,35229149,35229854,35229999],"created_at":"2023-03-20T07:09:13Z","created_at_i":1679296153,"num_comments":165,"objectID":"35228219","points":111,"story_id":35228219,"title":"Reassessing relative temporal lobe size in anthropoids and modern humans","updated_at":"2025-12-14T20:35:08Z","url":"https://onlinelibrary.wiley.com/doi/10.1002/ajpa.24712"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"abathologist"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"I am on the dev team for this project and would be happy to answer any questions and/or take note of any critical feedback :)
Here's a bit more detail:
Quint is a modern, executable specification language that is a particularly good fit for distributed systems, such as blockchain protocols, distributed databases, and p2p protocols. Quint combines the robust theoretical basis of the Temporal Logic of Actions (TLA) with state-of-the-art static analysis and development tooling."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["temporal"],"value":"Quint: A specification language based on the temporal logic of actions (TLA)"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/informalsystems/quint"}},"_tags":["story","author_abathologist","story_38694278"],"author":"abathologist","children":[38694279,38701125,38708492,38708651,38708652,38708729,38708869,38712176,38713304,38715354,38717039],"created_at":"2023-12-19T11:19:55Z","created_at_i":1702984795,"num_comments":35,"objectID":"38694278","points":110,"story_id":38694278,"story_text":"I am on the dev team for this project and would be happy to answer any questions and/or take note of any critical feedback :)
Here's a bit more detail:
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Curious what other signals people think would unlock better collaboration.
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Curious what other signals people think would unlock better collaboration.
Full back story: https://medium.com/@jeremie.lumbroso/teaching-ai-the-signifi...
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