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01001000 01100101 01101100 01101100 01101111 00100000

semantic_graph.connect(node_a, node_b)

{ "embedding": [0.023, -0.841, 0.192, 0.447] }

10110100 11001010 01010111 00101001 11110000 01100001

chunk(input, window=512, overlap=50)

[ 0.447, -0.033, 0.891, 0.124, -0.567, 0.284 ]

01110011 01110101 01101101 01101101 01100001 01110010

topics: ai · design · architecture · systems

cosine_sim(v1, v2) → 0.847

11010011 00101110 01111001 10001100 01010010 11100110

index.upsert(id, vector, metadata)

{ "summary": "Key findings on neural..." }

01001000 01100101 01101100 01101100 01101111 00100000

semantic_graph.connect(node_a, node_b)

{ "embedding": [0.023, -0.841, 0.192, 0.447] }

10110100 11001010 01010111 00101001 11110000 01100001

chunk(input, window=512, overlap=50)

[ 0.447, -0.033, 0.891, 0.124, -0.567, 0.284 ]

01110011 01110101 01101101 01101101 01100001 01110010

topics: ai · design · architecture · systems

cosine_sim(v1, v2) → 0.847

11010011 00101110 01111001 10001100 01010010 11100110

index.upsert(id, vector, metadata)

{ "summary": "Key findings on neural..." }

01001000 01100101 01101100 01101100 01101111 00100000

semantic_graph.connect(node_a, node_b)

{ "embedding": [0.023, -0.841, 0.192, 0.447] }

10110100 11001010 01010111 00101001 11110000 01100001

chunk(input, window=512, overlap=50)

[ 0.447, -0.033, 0.891, 0.124, -0.567, 0.284 ]

01110011 01110101 01101101 01101101 01100001 01110010

topics: ai · design · architecture · systems

cosine_sim(v1, v2) → 0.847

11010011 00101110 01111001 10001100 01010010 11100110

index.upsert(id, vector, metadata)

{ "summary": "Key findings on neural..." }

01001000 01100101 01101100 01101100 01101111 00100000

semantic_graph.connect(node_a, node_b)

{ "embedding": [0.023, -0.841, 0.192, 0.447] }

10110100 11001010 01010111 00101001 11110000 01100001

chunk(input, window=512, overlap=50)

[ 0.447, -0.033, 0.891, 0.124, -0.567, 0.284 ]

01110011 01110101 01101101 01101101 01100001 01110010

topics: ai · design · architecture · systems

cosine_sim(v1, v2) → 0.847

11010011 00101110 01111001 10001100 01010010 11100110

index.upsert(id, vector, metadata)

{ "summary": "Key findings on neural..." }

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youtube.com/watch?v=...

"Sam Altman discusses OpenAI's new reasoning model and what it means for AGI research..."

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Sam AltmanOpenAIAGIreasoning model

Summary: Sam Altman previews OpenAI's latest reasoning model, explaining how it approaches complex problems differently...

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Tweet about OpenAI DevDayTweet

Article: "The Race to AGI"Article

HN discussion on reasoningHN

+ 12 more connections found

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