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"I've never seen anything scarier than an LLM with tool calls." — Erik Meijer aka @HeadinTheBox

index_state ready data_status ok

AI Engineer· published 2026-07-13· 0:21:13· en-US· indexed 2026-08-11 03:01

Open on YouTube

Scene timeline

  1. Shot 0, 0:00 to 0:03, 1 of 1 keyframes kept
  2. Shot 1, 0:03 to 0:05, 1 of 1 keyframes kept
  3. Shot 2, 0:05 to 0:12, 1 of 1 keyframes kept
  4. Shot 3, 0:12 to 0:20, 1 of 1 keyframes kept
  5. Shot 4, 0:20 to 0:24, 1 of 1 keyframes kept
  6. Shot 5, 0:24 to 0:30, 1 of 1 keyframes kept
  7. Shot 6, 0:30 to 0:35, 1 of 1 keyframes kept
  8. Shot 7, 0:35 to 0:52, 1 of 1 keyframes kept
  9. Shot 8, 0:52 to 0:53, 1 of 1 keyframes kept
  10. Shot 9, 0:53 to 1:02, 1 of 1 keyframes kept
  11. Shot 10, 1:02 to 1:04, 1 of 1 keyframes kept
  12. Shot 11, 1:04 to 1:46, 1 of 1 keyframes kept
  13. Shot 12, 1:46 to 2:23, 1 of 1 keyframes kept
  14. Shot 13, 2:23 to 3:03, 1 of 1 keyframes kept
  15. Shot 14, 3:03 to 3:51, 1 of 1 keyframes kept
  16. Shot 15, 3:51 to 4:16, 1 of 1 keyframes kept
  17. Shot 16, 4:16 to 4:41, 0 of 1 keyframes kept
  18. Shot 17, 4:41 to 5:30, 1 of 1 keyframes kept
  19. Shot 18, 5:30 to 6:10, 1 of 1 keyframes kept
  20. Shot 19, 6:10 to 6:52, 1 of 1 keyframes kept
  21. Shot 20, 6:52 to 7:39, 1 of 1 keyframes kept
  22. Shot 21, 7:39 to 8:08, 1 of 1 keyframes kept
  23. Shot 22, 8:08 to 8:37, 1 of 1 keyframes kept
  24. Shot 23, 8:37 to 8:43, 1 of 1 keyframes kept
  25. Shot 24, 8:43 to 9:08, 1 of 1 keyframes kept
  26. Shot 25, 9:08 to 9:50, 1 of 1 keyframes kept
  27. Shot 26, 9:50 to 10:08, 1 of 1 keyframes kept
  28. Shot 27, 10:08 to 10:37, 1 of 1 keyframes kept
  29. Shot 28, 10:37 to 10:45, 1 of 1 keyframes kept
  30. Shot 29, 10:45 to 11:03, 1 of 1 keyframes kept
  31. Shot 30, 11:03 to 11:25, 1 of 1 keyframes kept
  32. Shot 31, 11:25 to 11:36, 1 of 1 keyframes kept
  33. Shot 32, 11:36 to 12:16, 1 of 1 keyframes kept
  34. Shot 33, 12:16 to 12:49, 1 of 1 keyframes kept
  35. Shot 34, 12:49 to 13:20, 1 of 1 keyframes kept
  36. Shot 35, 13:20 to 13:56, 1 of 1 keyframes kept
  37. Shot 36, 13:56 to 14:33, 1 of 1 keyframes kept
  38. Shot 37, 14:33 to 15:05, 1 of 1 keyframes kept
  39. Shot 38, 15:05 to 15:21, 1 of 1 keyframes kept
  40. Shot 39, 15:21 to 15:53, 1 of 1 keyframes kept
  41. Shot 40, 15:53 to 16:33, 1 of 1 keyframes kept
  42. Shot 41, 16:33 to 17:01, 1 of 1 keyframes kept
  43. Shot 42, 17:01 to 17:34, 1 of 1 keyframes kept
  44. Shot 43, 17:34 to 17:43, 1 of 1 keyframes kept
  45. Shot 44, 17:43 to 17:52, 1 of 1 keyframes kept
  46. Shot 45, 17:52 to 18:28, 1 of 1 keyframes kept
  47. Shot 46, 18:28 to 18:45, 1 of 1 keyframes kept
  48. Shot 47, 18:45 to 19:29, 1 of 1 keyframes kept
  49. Shot 48, 19:29 to 20:03, 1 of 1 keyframes kept
  50. Shot 49, 20:03 to 20:05, 0 of 1 keyframes kept
  51. Shot 50, 20:05 to 20:10, 0 of 1 keyframes kept
  52. Shot 51, 20:10 to 20:11, 1 of 1 keyframes kept
  53. Shot 52, 20:11 to 20:18, 1 of 1 keyframes kept
  54. Shot 53, 20:18 to 20:40, 1 of 1 keyframes kept
  55. Shot 54, 20:40 to 20:42, 1 of 1 keyframes kept
  56. Shot 55, 20:42 to 20:44, 1 of 1 keyframes kept
  57. Shot 56, 20:44 to 20:46, 1 of 1 keyframes kept
  58. Shot 57, 20:46 to 20:48, 1 of 1 keyframes kept
  59. Shot 58, 20:48 to 20:49, 0 of 1 keyframes kept
  60. Shot 59, 20:49 to 20:56, 1 of 1 keyframes kept
  61. Shot 60, 20:56 to 21:11, 0 of 1 keyframes kept
  62. Shot 61, 21:11 to 21:12, 1 of 1 keyframes kept

62 shot(s).

keyframes kept every frame deduplicated

What was stored

cues
206
whisperx 206
chunks
36
from 206 cues
keyframes
57
kept of 62 captured
frames with text
56
1,817 lines read
chapters
10
from the source metadata
keyframe bytes
12.2 MB
word timings on 206 cues

Provenance

Each pipeline stage, its state and the model that produced it
stage state model started took
fetch done — 2026-08-11 02:56 1m 27s
stt done — 2026-08-11 02:58 23s
chunk done — 2026-08-11 02:58 0s
text_embed done — 2026-08-11 02:58 0s
keyframe done — 2026-08-11 02:58 2m 29s
ocr done — 2026-08-11 03:00 25s
frame_embed done — 2026-08-11 03:01 9s

Frames, and what the machine read

  • 0:02 #0 done2 line(s)

    shot 0·sharpness 456.7

    1. AlEngineer0.96
    2. World's Fair0.97

  • 0:03 #1 done2 line(s)

    shot 1·sharpness 663.3

    1. AlEngineer0.95
    2. World's Fair0.99

  • 0:10 #2 done24 line(s)

    shot 2·sharpness 2742.3

    1. LAB & PLATINUM SPONSORS0.99
    2. Amazon AGI Lab0.98
    3. ANTHROP\C1.00
    4. Google DeepMind1.00
    5. MINIMAX0.93
    6. OpenAI0.92
    7. Akamai1.00
    8. arize0.92
    9. aws1.00
    10. Braintrust bright data0.98
    11. B1.00
    12. Browserbase1.00
    13. docker1.00
    14. :neo4j0.90
    15. ORACLE1.00
    16. PayPal1.00
    17. qodo1.00
    18. reducto1.00
    19. Sonar1.00
    20. Makers of1.00
    21. togetherai1.00
    22. Unblocked1.00
    23. WorkOS1.00
    24. SonarQube1.00

  • 0:17 #3 done2 line(s)

    shot 3·sharpness 244.8

    1. Leibniz Labs1.00
    2. AF0.98

  • 0:23 #4 done4 line(s)

    shot 4·sharpness 532.5

    1. ERIKMEUJER0.91
    2. RESEARCH SCHOLAR1.00
    3. Leibniz Labs0.99
    4. AIE1.00

  • 0:26 #5 done4 line(s)

    shot 5·sharpness 811.1

    1. ERIKMEJUJER0.90
    2. RESEARCH SCHOLAR1.00
    3. Leibniz Labs0.99
    4. AIE1.00

  • 0:32 #6 done206 line(s)

    shot 6·sharpness 2928.4

    1. Id's Far0.82
    2. ORACLE1.00
    3. World's Fair0.96
    4. arize1.00
    5. World's Fair0.97
    6. Google DeepMind0.96
    7. World's Far0.97
    8. :neo4j0.96
    9. World's Fair0.96
    10. Z.AI0.98
    11. World's Far0.97
    12. bright data1.00
    13. World's Fair0.97
    14. Browserbase1.00
    15. compute co.0.98
    16. World's Fair0.91
    17. extend1.00
    18. World's Fair0.95
    19. vast.ai1.00
    20. World's Far0.95
    21. Ref.1.00
    22. World's Far0.81
    23. RedHat1.00
    24. World's Far0.96
    25. mezmo*0.97
    26. World's Fair0.97
    27. stigg1.00
    28. World's Fair0.96
    29. Id's Fair0.95
    30. RELAI0.91
    31. World's Fair0.94
    32. VAPI0.98
    33. World's Fair0.98
    34. Modal1.00
    35. World's Fair0.95
    36. promptql0.97
    37. World's Fair0.99
    38. THE VELOCITY ROOM0.95
    39. World's Fair0.95
    40. fiddler1.00
    41. World's Fair0.97
    42. Superconductor1.00
    43. urreale0.94
    44. World's Fair0.90
    45. ZERO0.94
    46. World's Fair0.93
    47. AlEngineer0.96
    48. comet1.00
    49. World's Far0.95
    50. authe1.00
    51. World's Far0.96
    52. Id's Fair0.96
    53. SOIO.IO0.85
    54. World's Fair0.95
    55. granica1.00
    56. World's Fair0.98
    57. dash00.98
    58. World's Far0.96
    59. PRIOR1.00
    60. jitalOcean0.99
    61. World's Fair0.95
    62. Vence0.99
    63. World's Fair0.92
    64. World's Fair1.00
    65. POSTMAN1.00
    66. World's Fair0.95
    67. Composio1.00
    68. World's Fair0.98
    69. Id's Fair0.90
    70. Modular1.00
    71. World's Fair0.95
    72. MERGE1.00
    73. World's Fair0.97
    74. AUTOMATTIC1.00
    75. World's Fair0.93
    76. Buildkite1.00
    77. gabyteDB1.00
    78. World's Fair0.98
    79. Zed0.99
    80. World's Fair0.94
    81. Keycard1.00
    82. World's Fair0.93
    83. Meticulous1.00
    84. World's Fair0.93
    85. Id's Fair0.91
    86. Daytona1.00
    87. World's Fair0.93
    88. twillo0.93
    89. World's Far0.93
    90. G GRAVITEE0.92
    91. World's Fair0.91
    92. PlanetScale1.00
    93. Temporal0.95
    94. World's Fair0.97
    95. Llamalndex0.99
    96. World's Fair0.99
    97. INNGEST0.95
    98. World's Fair0.94
    99. © BAND0.86
    100. World's Fair0.97
    101. Cleric1.00
    102. World's Fair0.96
    103. À ATLASSIAN0.96
    104. World's Fair0.98
    105. FACTORY1.00
    106. World's Fair0.98
    107. Id's Fair0.94
    108. baseten1.00
    109. World's Fair0.97
    110. Snorkel1.00
    111. World's Far0.97
    112. Z.AI0.97
    113. World's Fair0.95
    114. qodo0.96
    115. World's Fair0.96
    116. PayPal1.00
    117. World's Fair0.97
    118. Gradium1.00
    119. World's Fair0.97
    120. ANTHROPIC1.00
    121. n AGI Lab0.98
    122. World's Far0.95
    123. Browserbase1.00
    124. World's Fair0.98
    125. :neo4j0.94
    126. World's Fair0.95
    127. Google DeepMind1.00
    128. World's Far0.91
    129. arize1.00
    130. World's Far0.95
    131. reducto1.00
    132. World's Fair0.96
    133. Microsoft0.93
    134. World's Fair0.96
    135. Id's Fair0.85
    136. OpenAl0.97
    137. World's Fair0.96
    138. WorkOs0.95
    139. World's Fair0.97
    140. Amazon AGI Lab0.99
    141. World's Fair0.96
    142. Microsoft0.99
    143. World's Fair0.95
    144. ORACLE1.00
    145. World's Far0.91
    146. bright data0.99
    147. World's Fair0.95
    148. Google DeepMind1.00
    149. licrosoft0.99
    150. World's Fair0.97
    151. docker1.00
    152. World's Fair0.95
    153. Braintrust1.00
    154. World's Fair0.97
    155. OpenAl0.97
    156. id's Fair0.90
    157. MINIMAX0.98
    158. World's Fair0.99
    159. Z.AI0.97
    160. World's Fair0.99
    161. ANTHROPIC1.00
    162. World's Fair0.98
    163. Id's Fair0.96
    164. snyk1.00
    165. World's Fair0.95
    166. aws0.99
    167. World's Far0.96
    168. togetherai1.00
    169. World's0.85
    170. CAkamai0.96
    171. World's Fair0.98
    172. Unblocked0.96
    173. Worldia0.72
    174. Airbyte1.00
    175. Lightrun1.00
    176. ain0.99
    177. World's Fair0.97
    178. CLOUDP0.86
    179. World's Far0.96
    180. DAT0.99
    181. orld's Fair0.86
    182. Resolve.ai1.00
    183. World's Fair0.93
    184. World's Fal0.90
    185. Op0.99
    186. Worild's Fair0.89
    187. Id'sFair0.89
    188. LanceDB1.00
    189. World's Fair0.97
    190. builderio0.93
    191. World's Fa0.94
    192. Ravenna1.00
    193. World's Fair0.97
    194. CopilotKit0.99
    195. Wor0.98
    196. World's Fair0.95
    197. cupe0.82
    198. World's Fair0.97
    199. TOPK1.00
    200. World's Fair0.94
    201. cognee1.00
    202. orid's Far0.89
    203. eNCORD0.93
    204. World's Fair0.94
    205. rid's Fa0.86
    206. World's Fair0.87

  • 0:42 #7 done11 line(s)

    shot 7·sharpness 557.2

    1. World's Fair0.96
    2. Microsoft1.00
    3. AlEngineer0.98
    4. OpenAI0.93
    5. World's Fair0.97
    6. AlEngineer0.96
    7. World's F0.89
    8. amai1.00
    9. AlEngineer0.98
    10. DATAD1.00
    11. World's Fair0.99

  • 0:52 #8 done9 line(s)

    shot 8·sharpness 474.3

    1. zon AGI Lab0.95
    2. World'Fair0.95
    3. AlEngineer0.97
    4. orld's Fair0.99
    5. Wo0.80
    6. together.ai0.98
    7. AIEngineer0.97
    8. orld's Fair0.99
    9. Wo0.70

  • 0:58 #9 done13 line(s)

    shot 9·sharpness 519.9

    1. Lab1.00
    2. World's Fair1.00
    3. Microsof1.00
    4. AlEngineer0.99
    5. Ope0.95
    6. r1.00
    7. World's Fair1.00
    8. ai1.00
    9. Wc0.97
    10. Akamai1.00
    11. AlEngineer0.97
    12. 福DA0.58
    13. World's Fair0.99

  • 1:03 #10 done18 line(s)

    shot 10·sharpness 1861.3

    1. AlEngineer0.99
    2. World's Fair0.98
    3. In Code They Speak0.98
    4. In Proof We Trust0.96
    5. Erik Meijer0.98
    6. I'sFair0.99
    7. Micr1.00
    8. AlEnginee1.00
    9. enAl0.92
    10. orld's0.90
    11. gineer1.00
    12. I'sFa0.99
    13. In Code They Act, In Proof We Trust1.00
    14. kan0.92
    15. jinee0.98
    16. Erik Meijer / Research Scholar Leibniz Labs0.98
    17. TADOG0.94
    18. l'e0.89

  • 1:29 #11 done80 line(s)

    shot 11·sharpness 2766.8

    1. THIS IS NOT A1.00
    2. UNIVERSALIS1.00
    3. Universalis Plan (AST)1.00
    4. World's Fair0.98
    5. AlEngineer0.98
    6. SOMEONE MORE CREDIBLE0.99
    7. CONVICTION TO BUILD ON1.00
    8. THAN ME WILL FIND1.00
    9. PRODUCT PITCH.1.00
    10. THESE IDEAS.1.00
    11. A LANGUAGE FOR INTENT,0.98
    12. VERIFICATION & AGENTIC COMPUTE1.00
    13. AUTOMIND1.00
    14. THE SAFE, VERIFIABLE0.99
    15. Ensure(IsSafe)0.94
    16. Switch (user.intent) {0.94
    17. case Ask -> SearchDocs(q)0.95
    18. case Email →> DraftEmail(g)0.92
    19. case Compute -> RunTool (t)0.97
    20. ✓VERIFIABLE0.95
    21. - Tools are getting richer0.91
    22. Risks are getting real0.95
    23. •Verification is possible0.94
    24. - LLMs are getting better0.94
    25. WHY NOW?1.00
    26. (and necessary)1.00
    27. FIRST-CLASS IDEAS1.00
    28. AGENT RUNTIME1.00
    29. Properties1.00
    30. No data exiltration0.97
    31. VISION1.00
    32. PRESENTED BY0.98
    33. Conviction1.00
    34. are easy.0.99
    35. is rare.0.99
    36. Ideas1.00
    37. √ Intent as Code0.96
    38. Formal Semantics0.97
    39. Verification by Defoult0.97
    40. ✓ Composable Tools0.86
    41. V Transperent Execution0.86
    42. Human in the loop0.99
    43. Provable safety guarantees0.95
    44. No unauthorized actions0.96
    45. Safe autonomy for1.00
    46. knowledge work at1.00
    47. enterprise scale.0.97
    48. Human-Al Partrership0.96
    49. ANOTHER1.00
    50. Microsoft1.00
    51. LLM WRAPPER?0.96
    52. NOT YET1.00
    53. 州+10.76
    54. FAMOUS1.00
    55. 1d'sFair0.96
    56. Mic1.00
    57. VC FUNDAMENTALS0.99
    58. MARKET1.00
    59. TEAM0.91
    60. CALL ME WHEN0.98
    61. penAl0.89
    62. World1.00
    63. AlEngi0.98
    64. WHEN YOU HAVE0.99
    65. COME BACK1.00
    66. REVENUE1.00
    67. EXIT1.00
    68. MOAT1.00
    69. TRACTION0.99
    70. OPENAI BUILDS1.00
    71. IT FIRST1.00
    72. Id's Fa0.93
    73. IEngineer0.93
    74. Aka0.97
    75. In Code They Act, In Proof We Trust1.00
    76. AlEngi0.98
    77. Erik Meijer/ Research Scholar0.97
    78. Leibniz Labs0.98
    79. ATADO0.95
    80. AAI_-J_U0.51

  • 2:08 #12 done18 line(s)

    shot 12·sharpness 4438.8

    1. I made a serious error — the0.99
    2. AlEngineer0.99
    3. World'sFair1.00
    4. perl -0 slurp mode with print1.00
    5. unless blanked the entire0.99
    6. PRESENTED BY1.00
    7. Microsoft1.00
    8. LlvmBackend.kt. Let me check1.00
    9. for any recovery source before1.00
    10. GI Lab0.89
    11. World'sFa1.00
    12. reconstructing1.00
    13. Fair1.00
    14. penAl0.96
    15. lei0.73
    16. Id'sFa0.96
    17. In Code They Act, In Proof We Trust1.00
    18. Erik Meijer / Research Scholar Leibniz Labs0.99

  • 2:58 #13 done56 line(s)

    shot 13·sharpness 4107.4

    1. HEADINTHEBOX0.99
    2. PERFORMANCE!1.00
    3. AlEngineer0.99
    4. PURITY!1.00
    5. PROOFS!1.00
    6. World's Fair0.99
    7. TEARS OUT HIS HEART-0.99
    8. THAT'S WHAT1.00
    9. MATTERS!0.94
    10. AGAIN!1.00
    11. FRIENDS SAY1.00
    12. HE HASN'T SLEPT1.00
    13. UNIVERSALIS1.00
    14. SINCE 1992.1.00
    15. PRESENTED BY0.98
    16. NEITHER HAS1.00
    17. HIS TYPE SYSTEM.0.98
    18. Microsoft1.00
    19. AUTOMIND1.00
    20. LINQ1.00
    21. TASK ORCHESTRATION1.00
    22. FOR AGENTS0.99
    23. TO-DO (IF ANY TIME LEFT):0.98
    24. Cω0.77
    25. DESIGN A LANGUAGE0.97
    26. d's Fair0.96
    27. Mici0.98
    28. HASKELL1.00
    29. COFFEE,1.00
    30. PLAY BASS0.95
    31. FIX THE TYPE SYSTEM0.96
    32. SAVE THE WORLD0.98
    33. MONDRIAN1.00
    34. CURIOSITY,1.00
    35. SLEEP1.00
    36. CANCER1.00
    37. VISUAL BASIC1.00
    38. [IN REMISSION)0.94
    39. ∀x.P(x)→3y,Q(x,y)0.88
    40. enAl0.85
    41. Vorld's0.90
    42. AlEngin0.96
    43. Context = RAM0.97
    44. RAG = Virtual Memory0.94
    45. Tools = ISA0.92
    46. (Horn clauses, baby))0.97
    47. LLM = Branch Predictor0.96
    48. Engineo0.96
    49. d's1.00
    50. Aka1.00
    51. In Code They Act, In Proof We Trust1.00
    52. AlEngin0.98
    53. Erik Meijer/ Research Scholar0.97
    54. Leibniz Labs0.96
    55. ATAD0.96
    56. 8__._D.0.57

  • 3:32 #14 done41 line(s)

    shot 14·sharpness 3044.2

    1. MYTHOS1.00
    2. How did we get here?1.00
    3. 月」0.89
    4. AlEngineer0.99
    5. PATRON SAINT1.00
    6. Well, I used to know you so well1.00
    7. World'sFair1.00
    8. OF CONTEXT,0.99
    9. COMPLETION1.00
    10. CHAOS &0.99
    11. Well, I think I know1.00
    12. How did we get here?0.96
    13. 月1.00
    14. CLAUDE1.00
    15. PRESENTED BY1.00
    16. ALIGNMENT1.00
    17. Microsoft1.00
    18. THEORIES1.00
    19. CONTEXT1.00
    20. WINDOW1.00
    21. TOKENS1.00
    22. WERE1.00
    23. SPENT1.00
    24. Fair1.00
    25. Microsc0.97
    26. AI0.79
    27. orld's Fa0.98
    28. AlEngineer0.97
    29. RESEARCHER1.00
    30. SAFETY1.00
    31. OPENAI0.93
    32. ANTHROPI1.00
    33. (HOPEFUL)1.00
    34. Fai0.84
    35. Akamai1.00
    36. In Code They Act, In Proof We Trust0.99
    37. AlEngineer0.99
    38. Erik Meijer/ Research Scholar0.97
    39. Leibniz Labs0.97
    40. DOG0.99
    41. rld'e Co0.88

  • 4:06 #15 done32 line(s)

    shot 15·sharpness 6562.6

    1. Machines1.00
    2. AlEngineer0.99
    3. World'sFair1.00
    4. of loving1.00
    5. def llm (q : Question) :0.94
    6. grace1.00
    7. (a: Answer)1.00
    8. BEWARE1.00
    9. OF1.00
    10. AI1.00
    11. PRESENTED BY0.98
    12. #eval llm "Summarize my0.98
    13. Microsoft1.00
    14. ANTHROPIC1.00
    15. last email from Sarah"1.00
    16. "Sarah says your meeting1.00
    17. CLAUDE1.00
    18. Id'sFair0.97
    19. Mici0.96
    20. with Sean is cancelled as1.00
    21. Tom will be taking over"1.00
    22. AlEngin0.98
    23. enA0.99
    24. World'0.93
    25. Engineer1.00
    26. Id's0.83
    27. Aka1.00
    28. In Code They Act, In Proof We Trust1.00
    29. AlEngin0.97
    30. Erik Meijer / Research ScholarLeibniz Labs0.96
    31. ATAR0.99
    32. World0.86

  • 4:34 #16 skipped

    shot 16·duplicate of #15

  • 5:20 #17 done20 line(s)

    shot 17·sharpness 9258.4

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Transcript

206 cues· 2,978 words· 15,782 chars

  1. 0:13 Please welcome to the stage the research scholar at Leibniz Labs, Eric Meyer.
  2. 0:37 Well, can you go back one slide?
  3. 0:42 Sorry.
  4. 0:43 All right.
  5. 0:44 Good afternoon, everybody.
  6. 0:46 Thanks for being here after a long day of talks, exhibits, side effects.
  7. 0:52 Oh, sorry, that was the side events.
  8. 0:55 I hope that you have as much fun watching this talk as I had creating it.
  9. 1:06 Let me first get this out of the way.
  10. 1:07 This is not a product pitch or announcement or anything.
  11. 1:11 It's a 20 minute tutorial of how you can use elementary type systems and compiler knowledge to make AI
  12. 1:21 provably safe, and I'm sharing all my secrets with you today, hopefully to inspire some of you that next year you will have a booth downstairs where you have created a provably safe agentic harness.
  13. 1:40 Or who knows, maybe some of you have already solved it, let me know, and then we can grab a coffee instead of doing this talk.
  14. 1:50 With that out of the way, let's get going.
  15. 1:54 While I was preparing these slides, and I'm sorry that I was multitasking, but I was vibe coding on the side, and then when my attention wait for a second because I was trying to convince the model to draw some pictures that it didn't want to do, and you will see some of these pictures later, you can guess which ones were rejected, suddenly, wham!
  16. 2:20 to delete one of my files.
  17. 2:23 And I'm sure this has happened to you before, or maybe not, maybe you always run everything with no permissions and then you say yes, yes, yes, but I like to live dangerously.
  18. 2:38 But I'm convinced that if there's anything between
  19. 2:43 the model's goal and where the model currently is, it will do everything that it can to reach that goal, including killing us or deleting your files or deleting your database.
  20. 2:56 So I think that these models are intrinsically very, very dangerous and we have to tame them.
  21. 3:02 So that's what my talk is about.
  22. 3:05 So let's start this story.
  23. 3:08 And it's, I think, a very, very sad story, but also a scary story of how we as an industry got to this point where we are about to let normal people, the general public,
  24. 3:23 give control of their computers, their finances, their whole personal lives over to AI agents.
  25. 3:31 And we don't have any protection in place.
  26. 3:34 I think that's very sad and very scary.
  27. 3:38 So let me tell you the story how we got there, and I will have some characters like Claude, and we will see Dario, Daniela, Sam, Bernie, but
  28. 3:52 The main character is our friendly pit Claude here.
  29. 3:58 I think you can all remember November 30, 2022.
  30. 4:04 This was kind of like a very special day in history, because this was the first time that you could speak to your computer.
  31. 4:11 You could say, summarize my emails, and it would, you know,
  32. 4:17 answer you in perfect English.
  33. 4:20 I think for me at least that was magic, but I think most of us didn't realize that by introducing this innocent looking function here, LLM, that takes a question and returns an answer, that that would open Pandora's box and that would change our history forever.
  34. 4:40 But before we go continue the story, this conference is called AI Engineer.
  35. 4:48 All right, so we are engineers and maybe we're the last generation of engineers that still
  36. 4:54 understand what this is, what code is.
  37. 4:57 Or maybe most of you have already forgotten what code is, because all your code is written by agents.
  38. 5:03 But if we look at this signature here, it says, the LLM takes a question, returns an answer.
  39. 5:09 The question and answers are not strings.
  40. 5:11 They are very complicated JSON structures, and they get more complicated every day, every time a new release of APIs comes out.
  41. 5:19 But for this talk, we can just assume that question and answer are just opaque types.
  42. 5:24 We don't care about how they look like.
  43. 5:27 We do care about what they represent.
  44. 5:33 Now, anyway, the euphoria of these LLMs as being great tools didn't last very long.
  45. 5:40 And just when we thought that we have eradicated the smallpox of computer science, SQL injection, it came back with a vengeance.
  46. 5:50 Because the bad guys discovered that you can trick LLMs using prompt injection, and LLMs have no distinction, make no distinction between code
  47. 6:00 and text, and so they are very, very easy to trick.
  48. 6:05 And this, I think, is a bigger problem than SQL injection ever was.
  49. 6:12 But it was not prompt injection only that made LLMs kind of have a bad rep. LLMs are trained on the whole internet, and there's a lot of good stuff on the internet, but also a lot of bad stuff, like how do you create a bomb?
  50. 6:28 How do you synthesize drugs?

Chapters

  1. 0:00 Introduction and purpose of the talk
  2. 1:54 The inherent dangers of AI and accidental file deletion
  3. 3:39 The history and impact of LLMs (the "Pandora's box")
  4. 5:36 The problem of prompt injection and model safety
  5. 7:03 Formal verification and using Lean for safety proofs
  6. 10:45 The introduction of tool calls and the leap into chaos
  7. 13:59 The "lethal trifecta" of AI risks
  8. 14:13 The proposed solution: "air-gapping" the agentic loop
  9. 16:36 Refying plans into programs and using Free Monads
  10. 19:17 The concept of proof-carrying code and summary

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