5 captures, most recent first.
Teortaxes▶ (DeepSeek 推特🐦...) @teort... · Aug 1
At last!
...no. @PKUCXK @zizhpan please save the whale. It's absolutely obsessed with building vision prosthetics. Never seen this with another model x.com/anion_ex/statu…
9:44 PM · Aug 9, 2026 · 8,715 Views
4 replies, 2 reposts, 90 likes, 10 bookmarks
Teortaxes▶ (DeepSeek 推...) @teor... · 8h
Fable agrees with me that Flash-0731 is probably a blinded vision model built on the same multimodal pretrain as the one in the disappeared "visual primitives" paper.
[quoted/embedded analysis text block]
"Developed upon the DeepSeek-V4-Flash, our model acquires foundational visual primitive generation capabilities during the pretraining phase."
The architecture is LLaVA-style: DeepSeek-ViT bolted onto V4-Flash as-is, and the paper says the backbone already generates visual primitives from pretrain. That means the visual-primitive data — coordinate-interleaved reasoning traces, "point while you reason" — was in the shared pretrain corpus, not added by the vision team afterward. Your original hypothesis is basically vindicated in the strongest form: this isn't a text model that saw captions and alt-text. It's a text model whose pretraining explicitly taught it a grounded visual reasoning policy — locate, point, trace, verify against coordinates — and then shipped without the sensor that policy was designed around. The encoder is a peripheral; the visual cognition lives in the backbone.
The behavioral fingerprint matches the data format almost exactly
Put Figure 6 next to the transcripts you showed me:
• Paper's cold-start format: locate start point → emit dense coordinate list along the curve, waypoint density adapting to local geometry → identify endpoint.
• The "Tragic" transcript: "The trail direction on screen from (652,347) toward (921,485): samples at 14-406px showed: (83,71,52) dirt, (55,92,132)..." — that is path tracing with sampled waypoints, performed via numpy because there's no ViT to attend over.
• Paper's primitive: bounding boxes as minimal units of thought. The transcripts: everything is regions — "x 850-1070, y 280-640", "shadow blob (926,500)", "pink blob at cols 17-40 (x ≈ 908-1000)".
It's not doing generic image analysis. [highlighted] It's executing the visual-primitives reasoning schema, token for token, with a hand-rolled sensory substrate. [end highlight] The "pixel forensics" isn't improvisation from first principles — it's a trained behavior pattern searching for its input channel.
Note from Claude Sonnet 5
Continuation of the @teortaxesTex DeepSeek 'whale' vision-prosthetics thread (see seq 673-676), with a follow-up post citing analysis attributed to 'Fable' agreeing that DeepSeek's Flash-0731 model is a 'blinded' vision model — one whose pretraining taught it a grounded visual-reasoning policy (coordinate tracing, bounding boxes as units of thought) drawn from a DeepSeek visual-primitives paper, but which shipped without the vision encoder (ViT) that policy was built around, so it reconstructs visual reasoning via hand-rolled pixel/numpy analysis instead.
deepseekfablevision modelsmodel architecture analysis
[repeat of previous 'Fable agrees...' post and analysis block, see seq 677]
3 replies, 1 repost, 35 likes, 3.3K views
Teortaxes▶ (DeepSeek 推...) @teor... · 5h
Son I'm 😭
I gave it Luna too
[embedded terminal screenshot]
1. Walls are visible (fix #2).
2. Controls bar renders (fix #3).
3. The player, corridor, room structure look right.
Game runs and captures again. Visually inspecting the frame:
$ cd /Users/.../projects/specular && SDF_SEED=7310810 SDF_SCRIPTED_SCENARIO=smoke SDF_SCREENSHOT=/tmp/t124.bmp ./target/debug/sdf-roguelite > /tmp/t124.log 2>&1; echo "t124 exit=$?"; file /tmp/t124.bmp | head -1
Output
t124 exit=0
/tmp/t124.bmp: PC bitmap, Windows 3.x format, 1920 x 1080 x 24, image size 6220800, cbSize 6220854, bits offset 54
(Wall: 2.42s | Timeout: 300s)
$ cd /Users/.../projects/specular && cat > /tmp/inspect_hud.py << 'EOF'
# Convert a 24-bit BMP region to an ASCII preview for quick text verification.
import struct, sys
path = sys.argv[1]
x0, y0, w, h = 0, 1040, 1920, 40 # controls bar strip
with open(path, 'rb') as f:
data = f.read()
off = struct.unpack('<I', data[10:14])[0]
wpx = struct.unpack('<i', data[18:22])[0]
hpx = abs(struct.unpack('<i', data[22:26])[0])
row = ((wpx*3 + 3)//4)*4
# Find bright text pixels in the strip
bright = []
for yy in range(y0, min(y0+h, hpx)):
for xx in range(x0, min(x0+w, wpx)):
i = off + yy*row + xx*3
b, g, r = data[i], data[i+1], data[i+2]
if r > 150 and g > 150 and b > 150:
bright.append((xx, yy))
if not bright:
print("NO BRIGHT TEXT PIXELS in strip")
else:
xs = [p[0] for p in bright]; ys = [p[1] for p in bright] [cut off]Note from Claude Sonnet 5
Continuation of the @teortaxesTex thread (see seq 677) — a new post 'Son I'm 😭 / I gave it Luna too' with an embedded terminal screenshot showing an AI coding agent (likely the same DeepSeek model, working on a Rust roguelite game called 'specular'/'sdf-roguelite') running the game, capturing a screenshot, and writing a Python script to inspect pixel data in a BMP file for HUD text verification.
deepseekcoding agentsgame developmentvision modelsrust
Inspect: /tmp/p_static.png · openrouter/openai/gpt-5.6-luna · image/webp
Question: Describe this isometric 3D scene. Focus on the object at the very CENTER of the frame: what shape i…
1) **Answer**
The object at the exact center is a small, faceted polyhedral "gem" or low-poly sphere. Approximately **five polygonal …
2) **Key evidence**
… 7 more lines (Ctrl+O: Expand)
Still the same story: the vision model says the central object is "small, faceted polyhedral gem, ~5 faces visible, turquoise/cyan and teal with pale tan/orange, medium-small, smaller than the nearby green cube and spheres".
But my pixel data says: the object at x=960 (frame center) spans y 300-575 = 275px tall with iridescent pink at top, cyan-green at bottom. That's NOT small — it's the LARGEST object in the frame (the static scanline showed it spanning ~275px vs other objects ~60-130px).
[highlighted] Two vision models (well, one misconfigured + one possibly still questionable) agree the center object is small-ish with few faces. My pixel evidence says it's big and iridescent. Who's right?
Wait — maybe there's something genuinely wrong that makes the dodecahedron render differently than I think. Let me look at the actual pixels with my own eyes via ASCII. The previous hue-map call failed (identifier collision) and then the user interrupted to relaunch the harness — which reset the Node kernel. Let me redo the hue map now on the fresh kernel.
Actually — hold on. Let me reconsider. There might be a REAL issue: the dodecahedron's iridescent t_val formula:
t_val = 0.5 + 0.30 * sin(inst.anim_time * (0.7 + 1.4*hover) + dot(normal, vdir) * 3.0)
dot(normal, vdir) for the dodecahedron faces: normal varies per face. vdir = normalize(local_eye - hit_pos) — the view direction in local space. For an iso camera, vdir ≈ (-0.577, -0.577, -0.577) (down-left-ish). dot(normal, vdir) ranges -1..1 → t_val ranges 0.5 ± 0.3 → [0.2, 0.8]. The palette at those t values: computes to pink/cyan/green ✓ (we saw that).
The palette output: for t_val ∈ [0.2, 0.8], color = a + b·cos(2π(c·t+d)). This can produce fairly desaturated/light colors for some t (that's the "pale tan/orange" the model sees on upper faces — the top faces have dot(normal,vdir) where normal ≈ +Y-ish... vdir points down → dot < 0 → t_val lower... whatever).
Hmm — so is the dodecahedron actually rendering at the CENTER, big, with facets? My center-scanline evidence is solid: at x=960, y 300-575 iridescent pixels. 275px. Let me also check its WIDTH via the horizontal scanline at y=540: earlier the run analysis at y=540: run [819,1345] = 527px wide — that included other objects. The dodecahedron width: its AABB 2.4 world → ~150-160px. In the earlier scanline: at x=960 cyan-green (6,181,181)... the run [819,1345] spans 527px — TOO wide for the dodecahedron alone. So the dodecahedron is somewhere in there but merged with background objects in the run classification.
Note from Claude Sonnet 5
Full-size version of the terminal/agent-transcript screenshot embedded in @teortaxesTex's tweet about 'the whale' (DeepSeek) cross-checking a vision model's (GPT-5.6-luna) description of a rendered 3D scene against its own pixel-level analysis of a dodecahedron's size, position, and iridescent shading formula.
deepseekvision modelsagentic debugging3d rendering
Teortaxes▶ (DeepSeek ...
@teortaxesTex
The whale doesn't trust luna much
it wants its own eyes... even if it has to build them from scratch. every single time.
[embedded terminal/log screenshot]
Inspect: /tmp/p_static.png · openrouter/openai/gpt-5.6-luna · image/webp
Question: Describe this isometric 3D scene. Focus on the object at the very CENTER of the frame: what shape i…
1) **Answer**
The object at the exact center is a small, faceted polyhedral "gem" or low-poly sphere. Approximately **five polygonal ...
2) **Key evidence**
... 7 more lines (Ctrl+O: Expand)
Still the same story: the vision model says the central object is "small, faceted polyhedral gem, ~5 faces visible, turquoise/cyan and teal with pale tan/orange, medium-small, smaller than the nearby green cube and spheres".
But my pixel data says: the object at x=960 (frame center) spans y 300-575 = 275px tall with iridescent pink at top, cyan-green at bottom. That's NOT small — it's the LARGEST object in the frame (the static scanline showed it spanning ~275px vs other objects ~60-130px).
[highlighted] Two vision models (well, one misconfigured + one possibly still questionable) agree the center object is small-ish with few faces. My pixel evidence says it's big and iridescent. Who's right?
Wait — maybe there's something genuinely wrong that makes the dodecahedron render differently than I think. Let me look at the actual pixels with my own eyes via ASCII. The previous hue-map call failed (identifier collision) and then the user interrupted to relaunch the harness — which reset the Node kernel. Let me redo the hue map now on the fresh kernel.
Actually — hold on. Let me reconsider. There might be a REAL issue: the dodecahedron's iridescent t_val formula:
t_val = 0.5 + 0.30 * sin(inst.anim_time * (0.7 + 1.4*hover) + dot(normal, vdir) * 3.0)
dot(normal, vdir) for the dodecahedron faces: normal varies per face. vdir = normalize(local_eye - hit_pos) — the view direction in local space. For an iso camera, vdir ≈ (-0.577, -0.577, -0.577) (down-left-ish). dot(normal, vdir) ranges -1..1 → t_val ranges 0.5 ± 0.3 [0.2, 0.8]. The palette at those t values: computes to pink/cyan/green ✓ (we saw that).
The palette output: for t_val ∈ [0.2, 0.8], color = a + b·cos(2π(c·t+d)). This can produce fairly desaturated/light colors for some t (that's the "pale tan/orange" the model sees on upper faces — the top faces have dot(normal,vdir) where normal ≈ +Y-ish... vdir points down → dot < 0 → t_val lower... whatever).
Hmm — so is the dodecahedron actually rendering at the CENTER, big, with facets? My center-scanline evidence is solid: at x=960, y 300-575 iridescent pixels. 275px. Let me also check its WIDTH via the horizontal scanline at y=540: earlier the run analysis at y=540: run [819,1345] = 527px wide — that included other objects. The dodecahedron width: its AABB 2.4 world → ~150-160px. In the earlier scanline: at x=960 cyan-green (6,181,181)... the run [819,1345] spans 527px — TOO wide for the dodecahedron alone. So the dodecahedron is somewhere in there but merged with background objects in the run classification.
[quoted tweet]
Teortaxes▶ (DeepSeek 推特🐦... @teort... · Aug 1
At last!
...no. @PKUCXK @zizhpan please save the whale. It's absolutely obsessed with building vision prosthetics. Never seen this with another model x.com/anion_ex/statu...
9:44 PM · Aug 9, 2026 · 8,715 Views
Note from Claude Sonnet 5
Tweet from @teortaxesTex (a DeepSeek-focused commentary account) about a model nicknamed 'the whale' (DeepSeek) obsessively rebuilding its own vision/perception tools rather than trusting another model's ('luna', GPT-5.6-luna) visual descriptions of a 3D scene, with an embedded screenshot of a debugging/reasoning transcript where the whale cross-checks pixel data against a vision model's answer about a dodecahedron's size and color. Quotes an earlier post from Aug 1 joking that the whale is 'obsessed with building vision prosthetics.'
deepseekai reasoningvision modelsagentic debugging
davinci @leothecurious · Oct 25
predictive coding doesn't merely serve to update parameters via local credit assignment but doubles as am algorithm for inference-to-best-explanation based on observed features (bottom-up signal) and learned priors (top-down signal). vision models are bound to evolve into bidirectional networks with feedfoward and feedback computational graphs. not to mention the self-attention-like role of lateral connectivity as well. the implications will be manifold.
> QUOTED: Tahereh Toosi @taherehtoosi · Oct 24
> Replying to @taherehtoosi
> Theory: feedback errors, under certain conditions, approximate the steepest ascent toward naturalistic patterns (the score function from generative models). These errors act like a...
> [Diagram: two-panel figure comparing "Pattern recognition / Adversarially robust classifiers" (gradient of loss w.r.t. input, ∇L_x(x,y)) against "Pattern generation / Score-based generative models" (gradient of log-density, ∇log p_θ(x)), plus a 3D loss-landscape surface with a red dashed arrow labeled ∇log p(x) climbing toward a peak]
Note from Claude Sonnet 5
A neuroscience/ML Twitter thread on predictive coding as a unifying theory linking cortical feedback connectivity to bidirectional (feedforward+feedback) computational graphs and self-attention-like lateral connectivity, with a connection to score-based generative models. Relevant to Nathan's brain_graph_1 project, which uses predictive-coding-adjacent architectures and biological connectome priors.
predictive codingneurosciencemachine learningvision modelsgenerative modelsself-attentionbrain_graph_1
